knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
options(max.print = 999999) # we will have large outputs

library(knitr)
library(rmarkdown)
library(tidyverse) #for data cleaning and visualization
library(psych) # does factor analysis 
library(GPArotation) #for additional matrix roration; but may not be necesary for the current aanalysis
library(nFactors) # find n of factors

library(MVN) # Multivariate normality test
library(mvoutlier) #multivariate outliers

library(lme4)
library(lmerTest)
library(emmeans)
library(MuMIn)
#emm_options(lmerTest.limit = 27378)


# loading dataset
btr_data <- here::here("dataset/xml_results_BTR58_2020-5-26.csv") %>%
  read_csv()


# data cleaning 
btr_indices <- btr_data[,7:dim(btr_data)[2]] #create a data set only including the ling indices

btr_selected_var <- as.data.frame(cbind(btr_data[,1:6], btr_indices[, colSums(btr_indices != 0) > 0])) #filter it and combine them again

btr_filtered <- btr_selected_var %>%
  filter(nwords > 50)  # lengths is set 100 words

btr_filtered <- as.data.frame(cbind(btr_filtered[,1:6], btr_filtered[,c("nwords","wrd_length")], btr_filtered[,9:dim(btr_filtered)[2]]))  #swapping nwoards and wrdlengths column (since we do not use nwords in EFA)

#setting up the variable 
attach(btr_filtered)

# Wedelieted variables based on the distributions.
verbs_delete <- c("modal_necessity", "intransitive_activity_phrasal_verb", "intransitive_occurence_phrasal_verb", "intransitive_aspectual_phrasal_verb", "copular_phrasal_verb", "transitive_activity_phrasal_verb", "transitive_mental_phrasal_verb", "transitive_communication_phrasal_verb")

adj_delete <- c("size_attributive_adj", "time_attributive_adj", "color_attributive_adj", "evaluative_attributive_adj", "relational_attributive_adj", "topical__attributive_adj", "attitudinal_adj", "likelihood_adj", "certainty_adj", "ability_willingness_adj", "personal_affect_adj", "ease_difficulty_adj")

adv_delete <- c("attitudinal_adverb", "likelihood_adverb", "nonfactive_adverb", "hedges_adverb")

others_delete <- c("wh_relative_obj_clause", "that_verb_clause_nonfactive", "that_verb_clause_attitudinal", "that_verb_clause_likelihood", "that_adjective_clause", "that_adjective_clause_attitudinal", "that_adjective_clause_likelihood", "that_noun_clause_attitudinal", "that_noun_clause_factive", "that_noun_clause_likelihood", "that_noun_clause_nonfactive", "to_clause_verb_to_speech_act", "to_clause_verb_probability", "to_clause_verb_cognition", "to_clause_adjective_certainty", "to_clause_adjective_ability_willingness", "to_clause_adjective_certainty", "to_clause_adjective_ease_difficulty", "to_clause_adjective_evaluative", "to_clause_adjective_personal_affect")

delete_<- c("infinitive_prop", "pp_all" )

btr_filtered <- btr_filtered[, !(names(btr_filtered) %in% verbs_delete)]
btr_filtered <- btr_filtered[, !(names(btr_filtered) %in% adj_delete)]
btr_filtered <- btr_filtered[, !(names(btr_filtered) %in% adv_delete)]
btr_filtered <- btr_filtered[, !(names(btr_filtered) %in% others_delete)]
btr_filtered <- btr_filtered[, !(names(btr_filtered) %in% delete_)]

head(btr_filtered)
# count the number of columns
n_col <- dim(btr_filtered)[2]

MOOC_NonMOOC_category <- here::here("dataset/BTR58_NewCategory_2021-06-29.csv") %>%
  read_csv()

## These are in-house function for Multidimensional analysis

#1) *MD.reduce* takes Factor analysis results (fit object) and reduce the linguistic indices according to miminum factor loadings and communalities (see Biber, 1988, 2006).
MD.reduce <- function(fa_model, cutoff_loading, cut_off_communality) {
  #This function takes, Factor analysis model, a set of values for cutoff scores
  #output: a dataframe of linguistic indices, primary factor, factor loading, and communality 
  pattern_mat <- as.data.frame.matrix(fa_model$loadings)
  comu_vec <- as.data.frame(fa_model$communality)
  dim(pattern_mat)[1]
  # This is a holder
  Var_retained <- as.data.frame(cbind(pattern_mat[,0], rep(NA, dim(pattern_mat)[1]), rep(NA, dim(pattern_mat)[1]), rep(NA, dim(pattern_mat)[1])))
  colnames(Var_retained)[1] <- "factor_loaded"
  colnames(Var_retained)[2] <- "loading"
  colnames(Var_retained)[3] <- "communality"
  
  # iterate through the pattern matrix 
  for (f in 1:dim(pattern_mat)[2]) { #for each column
    for (i in 1:dim(pattern_mat)[1]) { # for each row
      if (abs(comu_vec[i,1]) >= cut_off_communality){ #check for communality criteria (above .15; Biber, 2006)
        if (abs(pattern_mat[i,f]) >= cutoff_loading & abs(pattern_mat[i,f]) == max(abs(pattern_mat[i,]))) {#
          Var_retained[i,1] <- names(pattern_mat)[f] # store the factor number -> change here 
          Var_retained[i,2] <- pattern_mat[i,f] #store the actual loading
          }
        }
     Var_retained[i,3] <- comu_vec[i,1] #store the actual communality
      }
  }
return(Var_retained)
}

# *MD.scores* calculates Factor scores as described in Biber (2006).

MD.scores <- function(fa_model,reduced_item_list, data) {
  #This function takes FA model, reduced variable list
  factor <- matrix(NA, nrow = dim(data)[1], ncol = dim(fa_model$loadings)[2]) #create an empty matrix for MD scores
  colnames(factor) <- colnames(fa_model$loadings)

  for (f in 1:fa_model$factors) { #iterate for a number of factors
    pos_var <- as.vector(rownames(reduced_item_list[which(reduced_item_list$factor_loaded == colnames(factor)[f] & reduced_item_list$loading >= 0),])) #extract list of var_name
    neg_var <- as.vector(rownames(reduced_item_list[which(reduced_item_list$factor_loaded == colnames(factor)[f] & reduced_item_list$loading <= 0),])) #extract list of var_name
    
    pos_indices <- data[, names(data) %in% pos_var] #subset of data based on variable name grabbed
    neg_indices <- data[, names(data) %in% neg_var] #subset of data based on variable name grabbed
    factor[,f] <- rowSums(scale(pos_indices)) - rowSums(scale(neg_indices)) #standardize them and add them up for each text
  }
  factor_score <- as.data.frame(cbind(data[,1:6], factor)) #finally, combine metadata and factor score for dataset
  return(factor_score)
}

# *MD.visualize* plots the scores based on MD.scores and print a list of variables relevant to the factor alongside.

MD.visualize <- function(fa_model, reduced_data, dataset, x, xorder = F, color, wrap =F, grid =F, xlab =F, ylab = F, legend = T) {
  n_factor <- fa_model$factors 
  factor_name <- sort(colnames(fa_model$loadings))
  score <- MD.scores(fa_model, reduced_data, dataset)
  xorder = tolower(x)
  
  if (xorder == 'nlengths'){
    xorder = reorder(unique(x), nchar(unique(x)))
    xorder
  }
  
  for (f in 1:n_factor) {
    print(paged_table(reduced_data[which(reduced_data$factor_loaded == factor_name[f]),]))
    
    
    if (xlab !=F){
      
    }
    if (ylab !=F){
      
    }

    plot <- ggplot(score, aes(x = reorder(tolower(x), xorder),  y = get(factor_name[f]), color = color)) +
                geom_jitter(alpha = .2) +
                geom_violin() +
                geom_boxplot(width = .3, outlier.alpha = 0) +
                theme_bw() +
                theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7)) +
                labs(y = paste("Dimension", f, sep = " "), x = paste(toupper(substring(deparse(substitute(x)), 1,1)), substring(deparse(substitute(x)), 2), sep="", collapse=" ") , color = deparse(substitute(color)) ) +
      coord_cartesian(clip="off")
    
    
    if (wrap != FALSE) {
      plot <- plot + facet_wrap(wrap, scales = "free_x")
    }
    
    if (grid != FALSE) {
      plot <- plot + facet_grid(grid, scales = "free_x")
    }
    
    if (legend == F) {
      plot <- plot + theme(legend.position = "none")
    }

    
    print(plot)
    cat("\r\n\r\n")

      }
} 

1 Setting up

1.1 Defining Functions

  1. MD.reduce takes Factor analysis results (fit object) and reduce the linguistic indices according to miminum factor loadings and communalities (see Biber, 1988, 2006).
  2. MD.scores calculates Factor scores as described in Biber (2006).
  3. MD.visualize plots the scores based on MD.scores and print a list of variables relevant to the factor alongside.

1.2 Dataset

head(btr_data)
xtabs(~btr_data$discipline)
## btr_data$discipline
##           business          education        engineering         humanities 
##               1271                531                774                674 
##    natural_science              other service_encounters     social_science 
##                627                 30                 22                858
xtabs(~btr_data$text_type)
## btr_data$text_type
##   announcements_discussions      assignment_description 
##                         403                         390 
##   classroom_management_talk           course_management 
##                          65                          21 
##                course_packs       instructional_reading 
##                          27                         663 
##         instructional_video                         lab 
##                        2217                          17 
##                     lecture                office_hours 
##                         177                          11 
## other_institutional_writing                        quiz 
##                          37                         306 
##           service_encounter                      slides 
##                          22                         140 
##                 study_group                    syllabus 
##                          25                         179 
##                   textbooks 
##                          87

1.2.1 checking the distribution of short files.

ggplot(btr_data[which(btr_data$nwords < 200),], aes(x = nwords)) +
  geom_histogram()

The final set ready to be analyzed.

paged_table(btr_filtered)

2 Multidimensional analysis (MDA)

2.1 Preliminary analysis

2.1.1 Distributions

n_ind <- dim(btr_filtered[,8:n_col])[2]
library(Routliers)
library(grid)

for (k in 1:n_ind) {
  breaks <- pretty(range(btr_filtered[, k+7]), n = nclass.FD(btr_filtered[, k+7]), min.n = 1)
  binwidth <- breaks[2] - breaks[1]
  # boundary for outliers
  out <- Routliers::outliers_mad(btr_filtered[,k+7], b = 1.483, threshold = 2.24) #see Kline (2015) p. 72 for the formula
  out$UL_CI_MAD
  p <- ggplot(btr_filtered[,8:n_col], aes(x = btr_filtered[,k + 7], y = ..density..)) +
    geom_histogram(binwidth = binwidth) +
    geom_density(color = "blue") +
    geom_vline(xintercept = out$UL_CI_MAD, color="orange", linetype = "longdash") +  
    geom_vline(xintercept = out$LL_CI_MAD, color="orange", linetype = "longdash") +  
    geom_vline(xintercept = mean(btr_filtered[,k+7]) + 3* sd(btr_filtered[,k+7]), color = "red", linetype = 'dashed') +
    geom_vline(xintercept = mean(btr_filtered[,k+7]) - 3* sd(btr_filtered[,k+7]), color = "red", linetype = 'dashed') +
    labs(title = colnames(btr_filtered)[7 + k], x = colnames(btr_filtered)[7 + k])
  print(p)
  cat("\r\n\r\n")
}

2.1.2 Data structure (showing first 6 rows)

head(btr_filtered[8:n_col])

2.1.3 Factorability of the dataset

  • KMO looks good (0.820)
KMO_btr <- KMO(btr_filtered[8:n_col])
KMO_btr$MSA
## [1] 0.816818
  • The index “Modal predictive” might behave badly, but no obvious issue was identified.
paged_table(as.data.frame(KMO_btr$MSAi))
  • Bartlett test indicated that correlation matrix is meaningful for FA.
bartlett.test(btr_filtered[8:n_col])
## 
##  Bartlett test of homogeneity of variances
## 
## data:  btr_filtered[8:n_col]
## Bartlett's K-squared = 1470539, df = 85, p-value < 2.2e-16

2.1.3.1 The number of unique cells in the variance covariance matrix

This is related to df in the SEM approach, just to show the size of the matrix

n_indicator = dim(btr_filtered[8:n_col])[2]
(n_indicator * (n_indicator + 1))/ 2
## [1] 3741

2.1.3.2 Multivatiate normality assumption (to examine which of PA or ML should be used)

  • Very very skewed data set. Therefore we will extract factors using Principal Axis factoring (PA), not maximum likelihood (ML).
normal <- psych::mardia(btr_filtered[8:n_col],na.rm = TRUE,plot=TRUE)

outlier <- psych::outlier(btr_filtered[8:n_col], plot = TRUE, bad = 10, na.rm = TRUE) #needs to check the cut-off value for this

2.2 Parallel analysis and scree plot

  • Parallel analysis indicated 21 factors could be extracted. However, when the sample size is huge, parallel analysis is known to produce very large amount of factors, leading to over-factoring.
  • Therefore, we then visually inspected the Scree plot, which suggested that the eigenvalue continue to drop until it reaches six-factor solution. This means that four and five factors may be under-factoring (which should be avoided; see Brown, 2015). Six and seven factor solution should not be different from each other.
  • Overall, we should not stop at 4 or 5, but we can stop at 6 or 7 depending on the interpretability.
screeplot <- fa.parallel(btr_filtered[8:n_col], fa = "fa", fm = "pa", n.iter=100, error.bars = T, sim = FALSE,quant=.95) 

## Parallel analysis suggests that the number of factors =  24  and the number of components =  NA
#factor method to use. (minres, ml, uls, wls, gls, pa)

2.3 Six-factor solution (Scree test preferred this one)

  • explained variance 31%
  • The root mean square of the residuals (RMSR) is 0.04
  • The df corrected root mean square of the residuals is 0.05
  • Tucker Lewis Index of factoring reliability = 0.475
  • RMSEA index = 0.082 and the 95 % confidence intervals are 0.081 0.083
PA6 <- fa(btr_filtered[8:n_col],nfactors= 6, n.iter=1, rotate="Promax",
          residuals=TRUE, SMC=TRUE, missing=TRUE,impute="median",
          min.err = 0.001,  max.iter = 50, symmetric=TRUE, warnings=TRUE, fm="pa",
          alpha=.05, p=.05, oblique.scores=TRUE)

PA6 <- fa.sort(PA6, polar=FALSE)


PA6loading <- as.data.frame(cbind(PA6$loadings, PA6$communality, PA6$uniquenesses,PA6$complexity))
#write.csv(PA6loading, "MD_result/PA6Loading_forETSreport.csv")

print(PA6, digit = 3)
## Factor Analysis using method =  pa
## Call: fa(r = btr_filtered[8:n_col], nfactors = 6, n.iter = 1, rotate = "Promax", 
##     residuals = TRUE, SMC = TRUE, missing = TRUE, impute = "median", 
##     min.err = 0.001, max.iter = 50, symmetric = TRUE, warnings = TRUE, 
##     fm = "pa", alpha = 0.05, p = 0.05, oblique.scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
##                                       PA1    PA3    PA4    PA2    PA6    PA5
## nonfinite_prop                     -0.813  0.221 -0.070  0.072  0.029 -0.185
## cc_clause                           0.774 -0.016 -0.081 -0.112 -0.005 -0.031
## contraction                         0.754  0.049 -0.210 -0.228 -0.065 -0.050
## emphatics                           0.750  0.003 -0.097 -0.111 -0.023 -0.108
## be_mv                               0.732 -0.055  0.029 -0.057 -0.024  0.022
## pp_demonstrative                    0.634  0.019  0.108 -0.198  0.000 -0.125
## nn_all                             -0.619 -0.341 -0.105  0.099 -0.087 -0.042
## pp1                                 0.613  0.151 -0.028 -0.074 -0.119 -0.035
## pp3_it                              0.585 -0.006 -0.077 -0.186 -0.011  0.114
## mean_verbal_deps                   -0.568  0.264 -0.093 -0.056  0.017 -0.156
## factive_adverb                      0.557  0.072 -0.104  0.067  0.048  0.016
## mlc                                -0.484 -0.177 -0.180 -0.032 -0.035 -0.199
## nn_abstract                        -0.442  0.077  0.026  0.307 -0.049 -0.190
## mltu                               -0.433  0.031 -0.073  0.048  0.152 -0.244
## mattr                              -0.411  0.133  0.000  0.284  0.046  0.249
## that_relative_clause                0.348  0.115 -0.120  0.234  0.339 -0.201
## det_nominal                         0.341  0.009  0.205 -0.238  0.175 -0.020
## adverbial_subordinator_causitive    0.308 -0.024  0.073 -0.069  0.043  0.123
## pp_indefinite                       0.292  0.098 -0.107 -0.135  0.046  0.129
## complementizer_that0                0.287  0.114  0.004 -0.009 -0.131  0.166
## jj_predicative                      0.278  0.152  0.113  0.087 -0.075  0.053
## past_participial_clause            -0.272 -0.105  0.082  0.055  0.079 -0.064
## amplifiers_adverb                   0.272 -0.017 -0.066  0.087  0.020  0.081
## discourse_particle                  0.251  0.026  0.228 -0.189 -0.006 -0.050
## agentless_passive                  -0.249 -0.079  0.248  0.038  0.086  0.047
## pv_do                               0.200  0.150  0.017 -0.037 -0.023  0.027
## conjuncts_adverb                   -0.144 -0.062  0.136 -0.118  0.072 -0.041
## downtoners_adverb                   0.124 -0.056  0.035 -0.027  0.070 -0.002
## all_phrasal_verbs                   0.123  0.039 -0.031 -0.104 -0.008  0.111
## to_clause                          -0.174  0.803  0.021  0.053  0.075  0.011
## verb                                0.183  0.695  0.173 -0.121  0.056  0.099
## non_past_tense                      0.316  0.665  0.036 -0.162 -0.022 -0.216
## to_clause_verb                      0.205  0.515 -0.015 -0.065  0.048 -0.050
## pp2                                -0.102  0.514 -0.208 -0.171 -0.039 -0.116
## mental_verb                         0.148  0.505  0.120  0.076 -0.105  0.068
## to_clause_verb_desire               0.095  0.447 -0.009 -0.023 -0.013 -0.078
## to_clause_noun                     -0.388  0.439  0.049  0.095  0.072  0.067
## dc_c                                0.102  0.413  0.240  0.076  0.400 -0.106
## activity_verb                      -0.080  0.391 -0.105 -0.360  0.101  0.054
## poss_nominal                       -0.089  0.372 -0.250  0.015 -0.031  0.290
## mean_nominal_deps                   0.017 -0.299 -0.165  0.088  0.135 -0.190
## modal_possibility                   0.041  0.292  0.166  0.028  0.063 -0.112
## to_clause_verb_to_causative        -0.002  0.279 -0.083  0.012  0.081  0.113
## aspectual_verb                     -0.159  0.267 -0.016 -0.042  0.032  0.002
## wh_clause                           0.098  0.259  0.106  0.088 -0.065 -0.056
## communication_verb                  0.018  0.246  0.069 -0.022 -0.009  0.170
## to_clause_adjective                -0.111  0.235  0.028  0.082  0.012  0.070
## split_aux                           0.143  0.192  0.035  0.011  0.042 -0.036
## causation_verb                     -0.053  0.192  0.089  0.087 -0.039 -0.127
## modal_predictive                   -0.187  0.189  0.035 -0.013 -0.030 -0.084
## by_passive                         -0.054 -0.175  0.078  0.065  0.102  0.070
## adverbial_subordinator_other       -0.018  0.168  0.158  0.010  0.043  0.080
## that_verb_clause                   -0.033  0.069  0.886 -0.039 -0.120 -0.017
## that_complement_clause              0.087  0.083  0.839  0.002 -0.082  0.005
## that_verb_clause_factive           -0.141  0.069  0.787 -0.117 -0.103 -0.078
## ccomp_c                             0.160  0.276  0.469  0.081 -0.178 -0.041
## existence_verb                     -0.043  0.040  0.234  0.104  0.027 -0.075
## that_noun_clause                    0.084  0.040  0.213  0.090  0.078  0.035
## wh_question                        -0.018  0.012 -0.168 -0.052 -0.115 -0.011
## wrd_length                         -0.551 -0.019 -0.003  0.588 -0.026  0.024
## cc_phrase                          -0.217  0.100 -0.108  0.561 -0.073 -0.056
## cc_nominal                          0.033  0.052 -0.148  0.526 -0.065 -0.115
## nominalization                     -0.375 -0.008  0.080  0.498 -0.097 -0.136
## jj_attributive                     -0.167 -0.239  0.035  0.441  0.049 -0.076
## amod_nominal                        0.104 -0.142  0.021  0.373  0.082 -0.008
## prep_phrase                        -0.213 -0.218  0.169  0.354  0.146  0.070
## adverbial_subordinator_conditional -0.006  0.174  0.044 -0.330 -0.004 -0.112
## prep_nominal                        0.055 -0.291  0.116  0.301  0.101  0.017
## time_adverbials                     0.225  0.014  0.142 -0.277  0.032 -0.090
## place_adverbials                    0.150  0.022  0.005 -0.271  0.000 -0.119
## nn_group                           -0.030 -0.035 -0.103  0.242  0.004  0.176
## nn_cognitive                       -0.187  0.069 -0.055  0.242 -0.031 -0.061
## nn_concrete                        -0.195 -0.053 -0.046 -0.202  0.029 -0.090
## nn_place                           -0.038 -0.136 -0.058  0.191 -0.058  0.065
## nn_quantity                        -0.046 -0.109 -0.004 -0.150 -0.014 -0.121
## occurrence_verb                     0.085 -0.060  0.048  0.127  0.015  0.040
## wh_relative_clause                 -0.151 -0.067  0.003 -0.208  0.824  0.435
## relcl_c                             0.038  0.134 -0.244  0.142  0.807 -0.111
## wh_relative_subj_clause            -0.123 -0.059 -0.008 -0.181  0.708  0.441
## relcl_nominal                       0.460  0.307 -0.153  0.032  0.596 -0.023
## wh_relative_prep_clause            -0.063 -0.032  0.030 -0.007  0.301  0.032
## pp3                                 0.140  0.038 -0.050  0.028  0.077  0.611
## past_tense                          0.184 -0.191  0.036 -0.093  0.061  0.577
## nn_animate                         -0.094  0.117 -0.145  0.239  0.022  0.439
## perfect_aspect                      0.068  0.062  0.119  0.042  0.043  0.246
## nn_technical                       -0.184 -0.161  0.063 -0.128 -0.002 -0.189
##                                        h2    u2  com
## nonfinite_prop                     0.7254 0.275 1.29
## cc_clause                          0.5873 0.413 1.07
## contraction                        0.6407 0.359 1.38
## emphatics                          0.5428 0.457 1.12
## be_mv                              0.5486 0.451 1.03
## pp_demonstrative                   0.5565 0.443 1.35
## nn_all                             0.8163 0.184 1.74
## pp1                                0.4810 0.519 1.25
## pp3_it                             0.4259 0.574 1.32
## mean_verbal_deps                   0.3700 0.630 1.68
## factive_adverb                     0.3074 0.693 1.15
## mlc                                0.5394 0.461 1.97
## nn_abstract                        0.3661 0.634 2.31
## mltu                               0.3314 0.669 1.98
## mattr                              0.2991 0.701 2.80
## that_relative_clause               0.3277 0.672 3.91
## det_nominal                        0.3560 0.644 3.11
## adverbial_subordinator_causitive   0.1633 0.837 1.62
## pp_indefinite                      0.1686 0.831 2.53
## complementizer_that0               0.1889 0.811 2.45
## jj_predicative                     0.1720 0.828 2.47
## past_participial_clause            0.1258 0.874 1.93
## amplifiers_adverb                  0.0764 0.924 1.56
## discourse_particle                 0.2241 0.776 2.97
## agentless_passive                  0.1133 0.887 2.56
## pv_do                              0.0998 0.900 2.02
## conjuncts_adverb                   0.0460 0.954 4.03
## downtoners_adverb                  0.0273 0.973 2.38
## all_phrasal_verbs                  0.0529 0.947 3.33
## to_clause                          0.5552 0.445 1.12
## verb                               0.7950 0.205 1.40
## non_past_tense                     0.7967 0.203 1.82
## to_clause_verb                     0.3993 0.601 1.39
## pp2                                0.3540 0.646 1.80
## mental_verb                        0.3849 0.615 1.49
## to_clause_verb_desire              0.2409 0.759 1.16
## to_clause_noun                     0.2124 0.788 2.22
## dc_c                               0.5075 0.492 2.97
## activity_verb                      0.2961 0.704 2.42
## poss_nominal                       0.2741 0.726 2.87
## mean_nominal_deps                  0.2192 0.781 3.07
## modal_possibility                  0.1417 0.858 2.11
## to_clause_verb_to_causative        0.0957 0.904 1.72
## aspectual_verb                     0.0665 0.933 1.73
## wh_clause                          0.1070 0.893 2.22
## communication_verb                 0.1212 0.879 2.00
## to_clause_adjective                0.0569 0.943 1.96
## split_aux                          0.0832 0.917 2.13
## causation_verb                     0.0480 0.952 3.04
## modal_predictive                   0.0525 0.947 2.52
## by_passive                         0.0720 0.928 3.07
## adverbial_subordinator_other       0.0707 0.929 2.62
## that_verb_clause                   0.7373 0.263 1.06
## that_complement_clause             0.7638 0.236 1.06
## that_verb_clause_factive           0.5464 0.454 1.18
## ccomp_c                            0.3955 0.604 2.34
## existence_verb                     0.0660 0.934 1.80
## that_noun_clause                   0.1001 0.900 2.18
## wh_question                        0.0616 0.938 2.04
## wrd_length                         0.7977 0.202 2.00
## cc_phrase                          0.4138 0.586 1.51
## cc_nominal                         0.2809 0.719 1.32
## nominalization                     0.4664 0.534 2.19
## jj_attributive                     0.4063 0.594 1.99
## amod_nominal                       0.1940 0.806 1.58
## prep_phrase                        0.3670 0.633 3.50
## adverbial_subordinator_conditional 0.1789 0.821 1.82
## prep_nominal                       0.2453 0.755 2.61
## time_adverbials                    0.2086 0.791 2.75
## place_adverbials                   0.1303 0.870 2.01
## nn_group                           0.1128 0.887 2.32
## nn_cognitive                       0.1179 0.882 2.39
## nn_concrete                        0.0932 0.907 2.69
## nn_place                           0.0828 0.917 2.64
## nn_quantity                        0.0544 0.946 3.04
## occurrence_verb                    0.0317 0.968 2.90
## wh_relative_clause                 0.7104 0.290 1.77
## relcl_c                            0.6962 0.304 1.36
## wh_relative_subj_clause            0.5628 0.437 1.92
## relcl_nominal                      0.7155 0.285 2.61
## wh_relative_prep_clause            0.0953 0.905 1.16
## pp3                                0.4315 0.569 1.17
## past_tense                         0.4043 0.596 1.53
## nn_animate                         0.2879 0.712 2.10
## perfect_aspect                     0.1182 0.882 1.93
## nn_technical                       0.1323 0.868 3.96
## 
##                         PA1   PA3   PA4   PA2   PA6   PA5
## SS loadings           9.214 5.236 3.365 3.760 2.747 2.316
## Proportion Var        0.107 0.061 0.039 0.044 0.032 0.027
## Cumulative Var        0.107 0.168 0.207 0.251 0.283 0.310
## Proportion Explained  0.346 0.197 0.126 0.141 0.103 0.087
## Cumulative Proportion 0.346 0.542 0.669 0.810 0.913 1.000
## 
##  With factor correlations of 
##        PA1    PA3    PA4    PA2    PA6    PA5
## PA1  1.000  0.382  0.398 -0.217  0.134  0.210
## PA3  0.382  1.000  0.106 -0.214 -0.081  0.136
## PA4  0.398  0.106  1.000 -0.042  0.289  0.128
## PA2 -0.217 -0.214 -0.042  1.000  0.274  0.071
## PA6  0.134 -0.081  0.289  0.274  1.000 -0.070
## PA5  0.210  0.136  0.128  0.071 -0.070  1.000
## 
## Mean item complexity =  2.1
## Test of the hypothesis that 6 factors are sufficient.
## 
## The degrees of freedom for the null model are  3655  and the objective function was  47.619 with Chi Square of  215834.4
## The degrees of freedom for the model are 3154  and the objective function was  21.914 
## 
## The root mean square of the residuals (RMSR) is  0.043 
## The df corrected root mean square of the residuals is  0.047 
## 
## The harmonic number of observations is  4563 with the empirical chi square  62578.41  with prob <  0 
## The total number of observations was  4563  with Likelihood Chi Square =  99235.98  with prob <  0 
## 
## Tucker Lewis Index of factoring reliability =  0.4748
## RMSEA index =  0.0817  and the 95 % confidence intervals are  0.0812 0.0822
## BIC =  72661.21
## Fit based upon off diagonal values = 0.938
## Measures of factor score adequacy             
##                                                     PA1   PA3   PA4   PA2   PA6
## Correlation of (regression) scores with factors   0.977 0.972 0.956 0.929 0.975
## Multiple R square of scores with factors          0.955 0.944 0.914 0.862 0.951
## Minimum correlation of possible factor scores     0.910 0.888 0.828 0.725 0.902
##                                                     PA5
## Correlation of (regression) scores with factors   0.914
## Multiple R square of scores with factors          0.836
## Minimum correlation of possible factor scores     0.671

2.3.1 Extracting primary loading linguistic indices

PA6_refined <- MD.reduce(PA6, .3, .15)
paged_table(PA6_refined)

2.3.2 Number of remaining indices

PA6_refined %>%
  na.omit() %>%
  dim()
## [1] 49  3

2.3.3 Calculating the Dimensional score

PA6_scores <- MD.scores(PA6, PA6_refined, btr_filtered)
paged_table(PA6_scores)
#write.csv(PA6_scores, "MD_result/Dimensional_score_PA6_5.0_ETSreport_modified.csv")

2.3.4 Dimension score: text-type * mode * learning_environment

MD.visualize(PA6, PA6_refined, btr_filtered, x = learning_environment, color = learning_environment, grid = ~mode,legend = T, xorder = "nlengths")
##                                  factor_loaded    loading communality
## nonfinite_prop                             PA1 -0.8132950   0.7253723
## cc_clause                                  PA1  0.7742338   0.5873482
## contraction                                PA1  0.7542997   0.6407286
## emphatics                                  PA1  0.7495712   0.5427764
## be_mv                                      PA1  0.7322963   0.5486265
## pp_demonstrative                           PA1  0.6335961   0.5565309
## nn_all                                     PA1 -0.6188822   0.8163238
## pp1                                        PA1  0.6131749   0.4810347
## pp3_it                                     PA1  0.5852639   0.4258781
## mean_verbal_deps                           PA1 -0.5684253   0.3699637
## factive_adverb                             PA1  0.5566128   0.3073590
## mlc                                        PA1 -0.4843049   0.5393716
## nn_abstract                                PA1 -0.4419376   0.3660817
## mltu                                       PA1 -0.4329341   0.3313769
## mattr                                      PA1 -0.4105833   0.2990983
## that_relative_clause                       PA1  0.3478179   0.3277435
## det_nominal                                PA1  0.3413118   0.3559768
## adverbial_subordinator_causitive           PA1  0.3082464   0.1632622

## 
## 
##                                    factor_loaded    loading communality
## wrd_length                                   PA2  0.5880763   0.7977080
## cc_phrase                                    PA2  0.5613025   0.4137876
## cc_nominal                                   PA2  0.5260028   0.2808598
## nominalization                               PA2  0.4980983   0.4664133
## jj_attributive                               PA2  0.4408125   0.4063362
## amod_nominal                                 PA2  0.3734195   0.1939523
## prep_phrase                                  PA2  0.3538703   0.3669912
## adverbial_subordinator_conditional           PA2 -0.3301145   0.1789314
## prep_nominal                                 PA2  0.3007542   0.2453117

## 
## 
##                       factor_loaded   loading communality
## to_clause                       PA3 0.8032894   0.5551529
## verb                            PA3 0.6946974   0.7949891
## non_past_tense                  PA3 0.6651300   0.7967250
## to_clause_verb                  PA3 0.5146971   0.3993298
## pp2                             PA3 0.5144921   0.3540182
## mental_verb                     PA3 0.5047000   0.3849005
## to_clause_verb_desire           PA3 0.4472352   0.2408542
## to_clause_noun                  PA3 0.4389121   0.2124316
## dc_c                            PA3 0.4125804   0.5075388
## activity_verb                   PA3 0.3906763   0.2960751
## poss_nominal                    PA3 0.3721704   0.2740686

## 
## 
##                          factor_loaded   loading communality
## that_verb_clause                   PA4 0.8855422   0.7373146
## that_complement_clause             PA4 0.8385487   0.7637723
## that_verb_clause_factive           PA4 0.7869121   0.5464035
## ccomp_c                            PA4 0.4685438   0.3955203

## 
## 
##            factor_loaded   loading communality
## pp3                  PA5 0.6110859   0.4314571
## past_tense           PA5 0.5767110   0.4043281
## nn_animate           PA5 0.4394147   0.2878991

## 
## 
##                         factor_loaded   loading communality
## wh_relative_clause                PA6 0.8238232   0.7104070
## relcl_c                           PA6 0.8071224   0.6962011
## wh_relative_subj_clause           PA6 0.7076527   0.5628490
## relcl_nominal                     PA6 0.5960646   0.7154814

MD.visualize(PA6, PA6_refined, btr_filtered, x = text_type, color = mode, grid = ~mode+learning_environment, legend = F, xorder = "nlengths")
##                                  factor_loaded    loading communality
## nonfinite_prop                             PA1 -0.8132950   0.7253723
## cc_clause                                  PA1  0.7742338   0.5873482
## contraction                                PA1  0.7542997   0.6407286
## emphatics                                  PA1  0.7495712   0.5427764
## be_mv                                      PA1  0.7322963   0.5486265
## pp_demonstrative                           PA1  0.6335961   0.5565309
## nn_all                                     PA1 -0.6188822   0.8163238
## pp1                                        PA1  0.6131749   0.4810347
## pp3_it                                     PA1  0.5852639   0.4258781
## mean_verbal_deps                           PA1 -0.5684253   0.3699637
## factive_adverb                             PA1  0.5566128   0.3073590
## mlc                                        PA1 -0.4843049   0.5393716
## nn_abstract                                PA1 -0.4419376   0.3660817
## mltu                                       PA1 -0.4329341   0.3313769
## mattr                                      PA1 -0.4105833   0.2990983
## that_relative_clause                       PA1  0.3478179   0.3277435
## det_nominal                                PA1  0.3413118   0.3559768
## adverbial_subordinator_causitive           PA1  0.3082464   0.1632622

## 
## 
##                                    factor_loaded    loading communality
## wrd_length                                   PA2  0.5880763   0.7977080
## cc_phrase                                    PA2  0.5613025   0.4137876
## cc_nominal                                   PA2  0.5260028   0.2808598
## nominalization                               PA2  0.4980983   0.4664133
## jj_attributive                               PA2  0.4408125   0.4063362
## amod_nominal                                 PA2  0.3734195   0.1939523
## prep_phrase                                  PA2  0.3538703   0.3669912
## adverbial_subordinator_conditional           PA2 -0.3301145   0.1789314
## prep_nominal                                 PA2  0.3007542   0.2453117

## 
## 
##                       factor_loaded   loading communality
## to_clause                       PA3 0.8032894   0.5551529
## verb                            PA3 0.6946974   0.7949891
## non_past_tense                  PA3 0.6651300   0.7967250
## to_clause_verb                  PA3 0.5146971   0.3993298
## pp2                             PA3 0.5144921   0.3540182
## mental_verb                     PA3 0.5047000   0.3849005
## to_clause_verb_desire           PA3 0.4472352   0.2408542
## to_clause_noun                  PA3 0.4389121   0.2124316
## dc_c                            PA3 0.4125804   0.5075388
## activity_verb                   PA3 0.3906763   0.2960751
## poss_nominal                    PA3 0.3721704   0.2740686

## 
## 
##                          factor_loaded   loading communality
## that_verb_clause                   PA4 0.8855422   0.7373146
## that_complement_clause             PA4 0.8385487   0.7637723
## that_verb_clause_factive           PA4 0.7869121   0.5464035
## ccomp_c                            PA4 0.4685438   0.3955203

## 
## 
##            factor_loaded   loading communality
## pp3                  PA5 0.6110859   0.4314571
## past_tense           PA5 0.5767110   0.4043281
## nn_animate           PA5 0.4394147   0.2878991

## 
## 
##                         factor_loaded   loading communality
## wh_relative_clause                PA6 0.8238232   0.7104070
## relcl_c                           PA6 0.8071224   0.6962011
## wh_relative_subj_clause           PA6 0.7076527   0.5628490
## relcl_nominal                     PA6 0.5960646   0.7154814

2.3.5 Dimension score: Discipline by mode

MD.visualize(PA6, PA6_refined, btr_filtered, x = discipline, color = discipline, grid = mode~learning_environment, legend = F, xorder = "nlengths")
##                                  factor_loaded    loading communality
## nonfinite_prop                             PA1 -0.8132950   0.7253723
## cc_clause                                  PA1  0.7742338   0.5873482
## contraction                                PA1  0.7542997   0.6407286
## emphatics                                  PA1  0.7495712   0.5427764
## be_mv                                      PA1  0.7322963   0.5486265
## pp_demonstrative                           PA1  0.6335961   0.5565309
## nn_all                                     PA1 -0.6188822   0.8163238
## pp1                                        PA1  0.6131749   0.4810347
## pp3_it                                     PA1  0.5852639   0.4258781
## mean_verbal_deps                           PA1 -0.5684253   0.3699637
## factive_adverb                             PA1  0.5566128   0.3073590
## mlc                                        PA1 -0.4843049   0.5393716
## nn_abstract                                PA1 -0.4419376   0.3660817
## mltu                                       PA1 -0.4329341   0.3313769
## mattr                                      PA1 -0.4105833   0.2990983
## that_relative_clause                       PA1  0.3478179   0.3277435
## det_nominal                                PA1  0.3413118   0.3559768
## adverbial_subordinator_causitive           PA1  0.3082464   0.1632622

## 
## 
##                                    factor_loaded    loading communality
## wrd_length                                   PA2  0.5880763   0.7977080
## cc_phrase                                    PA2  0.5613025   0.4137876
## cc_nominal                                   PA2  0.5260028   0.2808598
## nominalization                               PA2  0.4980983   0.4664133
## jj_attributive                               PA2  0.4408125   0.4063362
## amod_nominal                                 PA2  0.3734195   0.1939523
## prep_phrase                                  PA2  0.3538703   0.3669912
## adverbial_subordinator_conditional           PA2 -0.3301145   0.1789314
## prep_nominal                                 PA2  0.3007542   0.2453117

## 
## 
##                       factor_loaded   loading communality
## to_clause                       PA3 0.8032894   0.5551529
## verb                            PA3 0.6946974   0.7949891
## non_past_tense                  PA3 0.6651300   0.7967250
## to_clause_verb                  PA3 0.5146971   0.3993298
## pp2                             PA3 0.5144921   0.3540182
## mental_verb                     PA3 0.5047000   0.3849005
## to_clause_verb_desire           PA3 0.4472352   0.2408542
## to_clause_noun                  PA3 0.4389121   0.2124316
## dc_c                            PA3 0.4125804   0.5075388
## activity_verb                   PA3 0.3906763   0.2960751
## poss_nominal                    PA3 0.3721704   0.2740686

## 
## 
##                          factor_loaded   loading communality
## that_verb_clause                   PA4 0.8855422   0.7373146
## that_complement_clause             PA4 0.8385487   0.7637723
## that_verb_clause_factive           PA4 0.7869121   0.5464035
## ccomp_c                            PA4 0.4685438   0.3955203

## 
## 
##            factor_loaded   loading communality
## pp3                  PA5 0.6110859   0.4314571
## past_tense           PA5 0.5767110   0.4043281
## nn_animate           PA5 0.4394147   0.2878991

## 
## 
##                         factor_loaded   loading communality
## wh_relative_clause                PA6 0.8238232   0.7104070
## relcl_c                           PA6 0.8071224   0.6962011
## wh_relative_subj_clause           PA6 0.7076527   0.5628490
## relcl_nominal                     PA6 0.5960646   0.7154814

2.4 Seven-factor solution

Another better fit, but slight changes. We may be prioritizing the interpretability, since none of it is favored in statistical sense. - explained 33 % of the variance. - The root mean square of the residuals (RMSR) is 0.04 - The df corrected root mean square of the residuals is 0.04 - Tucker Lewis Index of factoring reliability = 0.503 - RMSEA index = 0.08 and the 95 % confidence intervals are 0.079 0.08

PA7 <- fa(btr_filtered[8:n_col],nfactors= 7, n.iter=1, rotate="Promax", scores="Bartlett", 
          residuals=TRUE, SMC=TRUE, missing=TRUE,impute="median",
          min.err = 0.001,  max.iter = 50, symmetric=TRUE, warnings=TRUE, fm="pa",
          alpha=.05, p=.05, oblique.scores=TRUE)

PA7 <- fa.sort(PA7, polar = FALSE)
print(PA7)
## Factor Analysis using method =  pa
## Call: fa(r = btr_filtered[8:n_col], nfactors = 7, n.iter = 1, rotate = "Promax", 
##     scores = "Bartlett", residuals = TRUE, SMC = TRUE, missing = TRUE, 
##     impute = "median", min.err = 0.001, max.iter = 50, symmetric = TRUE, 
##     warnings = TRUE, fm = "pa", alpha = 0.05, p = 0.05, oblique.scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
##                                      PA1   PA3   PA4   PA2   PA7   PA5   PA6
## nonfinite_prop                     -0.82  0.15 -0.05  0.04 -0.18 -0.16  0.00
## contraction                         0.82  0.02 -0.16 -0.04 -0.12 -0.02  0.04
## cc_clause                           0.80 -0.09 -0.01  0.04 -0.08  0.02  0.10
## emphatics                           0.78 -0.03 -0.05  0.01 -0.04 -0.08  0.12
## be_mv                               0.75 -0.01  0.02 -0.01  0.13  0.01  0.07
## wrd_length                         -0.73  0.00 -0.03 -0.02  0.28  0.06  0.31
## pp_demonstrative                    0.69  0.03  0.11  0.02 -0.01 -0.14  0.00
## nn_all                             -0.65 -0.41 -0.04 -0.08 -0.12  0.00 -0.02
## pp3_it                              0.64  0.04 -0.09 -0.02  0.02  0.09 -0.06
## pp1                                 0.62  0.08  0.03 -0.08 -0.10  0.03  0.12
## mean_verbal_deps                   -0.56  0.09  0.01  0.05 -0.38 -0.08 -0.02
## nn_abstract                        -0.53  0.03  0.04 -0.02  0.04 -0.14  0.22
## nominalization                     -0.53 -0.04  0.09 -0.07  0.17 -0.07  0.34
## factive_adverb                      0.52 -0.01 -0.03  0.09 -0.03  0.08  0.19
## mattr                              -0.51  0.12 -0.01  0.03  0.10  0.27  0.06
## mlc                                -0.50 -0.43  0.01  0.03 -0.47 -0.09  0.03
## det_nominal                         0.42  0.07  0.16  0.16  0.04 -0.09 -0.16
## to_clause_noun                     -0.42  0.41  0.03  0.06 -0.04  0.08  0.00
## prep_phrase                        -0.33 -0.20  0.16  0.14  0.24  0.07  0.13
## adverbial_subordinator_causitive    0.33  0.02  0.05  0.03  0.08  0.09 -0.06
## pp_indefinite                       0.32  0.08 -0.09  0.04 -0.08  0.14 -0.06
## discourse_particle                  0.32  0.08  0.18 -0.02  0.04 -0.10 -0.12
## time_adverbials                     0.31  0.01  0.14  0.03 -0.11 -0.12 -0.15
## past_participial_clause            -0.27 -0.01  0.01  0.05  0.16 -0.13 -0.05
## complementizer_that0                0.27  0.04  0.06 -0.12 -0.10  0.23  0.04
## nn_cognitive                       -0.26  0.03 -0.03 -0.01  0.03 -0.01  0.19
## jj_predicative                      0.25  0.18  0.08 -0.07  0.12  0.06  0.10
## amplifiers_adverb                   0.24 -0.01 -0.06  0.02  0.09  0.09  0.09
## place_adverbials                    0.24  0.02  0.01  0.00 -0.14 -0.14 -0.13
## pv_do                               0.21  0.14  0.02 -0.02 -0.02  0.04  0.02
## all_phrasal_verbs                   0.15  0.01 -0.01 -0.01 -0.09  0.12 -0.07
## downtoners_adverb                   0.14  0.01 -0.01  0.06  0.11 -0.05 -0.03
## verb                                0.23  0.84  0.02  0.02  0.14  0.02 -0.10
## to_clause                          -0.18  0.76 -0.02  0.07 -0.07  0.03  0.06
## non_past_tense                      0.39  0.70 -0.04 -0.01 -0.03 -0.24  0.05
## to_clause_verb                      0.23  0.51 -0.05  0.05 -0.04 -0.05  0.04
## activity_verb                       0.04  0.43 -0.16  0.06 -0.17 -0.01 -0.27
## modal_possibility                   0.05  0.41  0.05  0.04  0.20 -0.18  0.03
## to_clause_verb_desire               0.11  0.40 -0.01  0.00 -0.09 -0.05  0.07
## pp2                                -0.05  0.39 -0.15 -0.02 -0.35 -0.05  0.00
## mental_verb                         0.11  0.39  0.17 -0.08 -0.13  0.16  0.13
## mean_nominal_deps                  -0.01 -0.36 -0.10  0.17 -0.02 -0.17  0.14
## prep_nominal                       -0.05 -0.33  0.16  0.12  0.16  0.05  0.19
## to_clause_verb_to_causative        -0.01  0.27 -0.09  0.07 -0.03  0.12  0.00
## wh_clause                           0.07  0.25  0.09 -0.05  0.03 -0.03  0.11
## to_clause_adjective                -0.13  0.25  0.00  0.00  0.04  0.07  0.03
## modal_predictive                   -0.17  0.24 -0.02 -0.04  0.04 -0.11 -0.02
## causation_verb                     -0.07  0.23  0.04 -0.04  0.10 -0.14  0.09
## split_aux                           0.15  0.23  0.00  0.04  0.07 -0.05  0.04
## adverbial_subordinator_other       -0.02  0.22  0.10  0.02  0.10  0.04 -0.05
## aspectual_verb                     -0.15  0.22  0.00  0.04 -0.12  0.02 -0.03
## that_verb_clause                   -0.05 -0.02  0.92 -0.10 -0.05  0.01 -0.10
## that_complement_clause              0.05 -0.04  0.92 -0.06 -0.07  0.06 -0.04
## that_verb_clause_factive           -0.11  0.03  0.78 -0.10 -0.05 -0.09 -0.16
## ccomp_c                             0.11  0.14  0.54 -0.13 -0.11  0.05  0.12
## that_noun_clause                    0.04 -0.04  0.27  0.10 -0.02  0.08  0.06
## wh_question                         0.00  0.04 -0.19 -0.13 -0.02 -0.01 -0.01
## relcl_c                            -0.01  0.02 -0.16  0.85 -0.03 -0.11  0.14
## wh_relative_clause                 -0.10 -0.02 -0.03  0.75  0.06  0.30 -0.42
## wh_relative_subj_clause            -0.08 -0.01 -0.03  0.64  0.05  0.33 -0.38
## relcl_nominal                       0.44  0.22 -0.09  0.63 -0.02 -0.01  0.12
## dc_c                                0.05  0.23  0.35  0.47 -0.17 -0.04  0.13
## that_relative_clause                0.28  0.10 -0.10  0.38  0.15 -0.19  0.29
## wh_relative_prep_clause            -0.06 -0.03  0.03  0.29  0.04  0.00 -0.07
## jj_attributive                     -0.26  0.00 -0.13  0.01  0.61 -0.19  0.25
## amod_nominal                        0.02  0.04 -0.11  0.05  0.52 -0.09  0.25
## mltu                               -0.49 -0.29  0.16  0.26 -0.51 -0.11  0.12
## agentless_passive                  -0.24  0.14  0.08  0.02  0.37 -0.10 -0.15
## by_passive                         -0.06 -0.03 -0.02  0.06  0.27 -0.02 -0.06
## existence_verb                     -0.06  0.15  0.14  0.01  0.24 -0.14  0.03
## adverbial_subordinator_conditional  0.10  0.16  0.04 -0.01 -0.21 -0.13 -0.19
## occurrence_verb                     0.05  0.00  0.01  0.01  0.17  0.02  0.07
## nn_place                           -0.09 -0.09 -0.08 -0.07  0.16  0.06  0.10
## pp3                                 0.09 -0.03  0.02  0.05 -0.07  0.66 -0.13
## past_tense                          0.18 -0.22  0.09  0.03 -0.04  0.59 -0.23
## nn_animate                         -0.21 -0.03 -0.04  0.03 -0.11  0.56  0.08
## poss_nominal                       -0.13  0.19 -0.13 -0.02 -0.33  0.42  0.02
## nn_technical                       -0.12 -0.06 -0.01 -0.02  0.08 -0.27 -0.12
## nn_group                           -0.12 -0.11 -0.04  0.02  0.01  0.25  0.14
## perfect_aspect                      0.04  0.07  0.10  0.02  0.07  0.24 -0.05
## communication_verb                  0.01  0.19  0.09 -0.01 -0.09  0.20 -0.04
## nn_quantity                         0.01 -0.10 -0.01 -0.02 -0.07 -0.14 -0.09
## cc_nominal                         -0.14 -0.01 -0.10 -0.01  0.16 -0.02  0.47
## cc_phrase                          -0.40  0.03 -0.07 -0.03  0.14  0.05  0.43
## nn_concrete                        -0.12  0.02 -0.10  0.00 -0.03 -0.16 -0.18
## conjuncts_adverb                   -0.10 -0.02  0.10  0.05  0.02 -0.10 -0.14
##                                       h2   u2 com
## nonfinite_prop                     0.724 0.28 1.3
## contraction                        0.643 0.36 1.1
## cc_clause                          0.606 0.39 1.1
## emphatics                          0.548 0.45 1.1
## be_mv                              0.548 0.45 1.1
## wrd_length                         0.801 0.20 1.7
## pp_demonstrative                   0.556 0.44 1.1
## nn_all                             0.830 0.17 1.8
## pp3_it                             0.426 0.57 1.1
## pp1                                0.491 0.51 1.2
## mean_verbal_deps                   0.411 0.59 1.9
## nn_abstract                        0.365 0.63 1.5
## nominalization                     0.463 0.54 2.1
## factive_adverb                     0.325 0.68 1.4
## mattr                              0.298 0.70 1.8
## mlc                                0.686 0.31 3.0
## det_nominal                        0.358 0.64 2.2
## to_clause_noun                     0.212 0.79 2.2
## prep_phrase                        0.364 0.64 4.2
## adverbial_subordinator_causitive   0.164 0.84 1.4
## pp_indefinite                      0.169 0.83 1.9
## discourse_particle                 0.227 0.77 2.4
## time_adverbials                    0.209 0.79 2.6
## past_participial_clause            0.144 0.86 2.3
## complementizer_that0               0.199 0.80 2.9
## nn_cognitive                       0.119 0.88 1.9
## jj_predicative                     0.174 0.83 3.4
## amplifiers_adverb                  0.076 0.92 2.1
## place_adverbials                   0.130 0.87 3.0
## pv_do                              0.100 0.90 1.9
## all_phrasal_verbs                  0.054 0.95 3.2
## downtoners_adverb                  0.033 0.97 2.6
## verb                               0.891 0.11 1.2
## to_clause                          0.558 0.44 1.2
## non_past_tense                     0.822 0.18 1.8
## to_clause_verb                     0.402 0.60 1.5
## activity_verb                      0.309 0.69 2.5
## modal_possibility                  0.187 0.81 2.0
## to_clause_verb_desire              0.239 0.76 1.3
## pp2                                0.359 0.64 2.4
## mental_verb                        0.396 0.60 2.6
## mean_nominal_deps                  0.234 0.77 2.5
## prep_nominal                       0.255 0.74 3.2
## to_clause_verb_to_causative        0.096 0.90 1.9
## wh_clause                          0.107 0.89 2.1
## to_clause_adjective                0.060 0.94 1.8
## modal_predictive                   0.065 0.93 2.5
## causation_verb                     0.057 0.94 2.8
## split_aux                          0.089 0.91 2.3
## adverbial_subordinator_other       0.079 0.92 2.1
## aspectual_verb                     0.067 0.93 2.6
## that_verb_clause                   0.782 0.22 1.1
## that_complement_clause             0.850 0.15 1.0
## that_verb_clause_factive           0.553 0.45 1.2
## ccomp_c                            0.439 0.56 1.6
## that_noun_clause                   0.118 0.88 1.7
## wh_question                        0.065 0.93 1.9
## relcl_c                            0.719 0.28 1.2
## wh_relative_clause                 0.707 0.29 2.0
## wh_relative_subj_clause            0.560 0.44 2.3
## relcl_nominal                      0.721 0.28 2.2
## dc_c                               0.584 0.42 2.9
## that_relative_clause               0.321 0.68 4.2
## wh_relative_prep_clause            0.094 0.91 1.3
## jj_attributive                     0.543 0.46 2.1
## amod_nominal                       0.275 0.72 1.7
## mltu                               0.574 0.43 3.6
## agentless_passive                  0.217 0.78 2.8
## by_passive                         0.106 0.89 1.4
## existence_verb                     0.090 0.91 3.3
## adverbial_subordinator_conditional 0.178 0.82 4.2
## occurrence_verb                    0.036 0.96 1.5
## nn_place                           0.086 0.91 4.5
## pp3                                0.445 0.56 1.2
## past_tense                         0.413 0.59 1.9
## nn_animate                         0.331 0.67 1.4
## poss_nominal                       0.322 0.68 2.8
## nn_technical                       0.151 0.85 2.1
## nn_group                           0.123 0.88 2.7
## perfect_aspect                     0.117 0.88 2.0
## communication_verb                 0.124 0.88 2.9
## nn_quantity                        0.054 0.95 3.0
## cc_nominal                         0.279 0.72 1.5
## cc_phrase                          0.411 0.59 2.3
## nn_concrete                        0.104 0.90 3.5
## conjuncts_adverb                   0.048 0.95 3.9
## 
##                         PA1  PA3  PA4  PA2  PA7  PA5  PA6
## SS loadings           10.36 5.05 3.29 2.79 2.46 2.42 1.90
## Proportion Var         0.12 0.06 0.04 0.03 0.03 0.03 0.02
## Cumulative Var         0.12 0.18 0.22 0.25 0.28 0.31 0.33
## Proportion Explained   0.37 0.18 0.12 0.10 0.09 0.09 0.07
## Cumulative Proportion  0.37 0.55 0.66 0.76 0.85 0.93 1.00
## 
##  With factor correlations of 
##       PA1   PA3   PA4  PA2   PA7  PA5   PA6
## PA1  1.00  0.41  0.40 0.07 -0.12 0.19 -0.11
## PA3  0.41  1.00  0.26 0.01 -0.29 0.18  0.05
## PA4  0.40  0.26  1.00 0.26  0.17 0.14 -0.01
## PA2  0.07  0.01  0.26 1.00  0.23 0.13  0.21
## PA7 -0.12 -0.29  0.17 0.23  1.00 0.20 -0.12
## PA5  0.19  0.18  0.14 0.13  0.20 1.00  0.20
## PA6 -0.11  0.05 -0.01 0.21 -0.12 0.20  1.00
## 
## Mean item complexity =  2.2
## Test of the hypothesis that 7 factors are sufficient.
## 
## The degrees of freedom for the null model are  3655  and the objective function was  47.62 with Chi Square of  215834.4
## The degrees of freedom for the model are 3074  and the objective function was  20.21 
## 
## The root mean square of the residuals (RMSR) is  0.04 
## The df corrected root mean square of the residuals is  0.04 
## 
## The harmonic number of observations is  4563 with the empirical chi square  53053.04  with prob <  0 
## The total number of observations was  4563  with Likelihood Chi Square =  91524.66  with prob <  0 
## 
## Tucker Lewis Index of factoring reliability =  0.504
## RMSEA index =  0.079  and the 95 % confidence intervals are  0.079 0.08
## BIC =  65623.94
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy             
##                                                    PA1  PA3  PA4  PA2  PA7  PA5
## Correlation of (regression) scores with factors   0.98 0.99 0.97 0.98 0.94 0.91
## Multiple R square of scores with factors          0.96 0.98 0.94 0.95 0.88 0.83
## Minimum correlation of possible factor scores     0.93 0.96 0.88 0.91 0.76 0.67
##                                                    PA6
## Correlation of (regression) scores with factors   0.91
## Multiple R square of scores with factors          0.83
## Minimum correlation of possible factor scores     0.66
PA7_reduced <- MD.reduce(PA7, .3, .15)
paged_table(PA7_reduced)
PA7_reduced %>%
  na.omit() %>%
  dim()
## [1] 54  3
PA7_scores <- MD.scores(PA7, PA7_reduced, btr_filtered)
paged_table(PA7_scores)

2.4.1 Dimension score: text-type * mode * learning_environment

MD.visualize(PA7, PA7_reduced, btr_filtered, x = text_type, color = mode, grid = ~mode+learning_environment, legend = F)
##                                  factor_loaded    loading communality
## nonfinite_prop                             PA1 -0.8235353   0.7244426
## contraction                                PA1  0.8186383   0.6431457
## cc_clause                                  PA1  0.7961354   0.6064587
## emphatics                                  PA1  0.7795884   0.5482211
## be_mv                                      PA1  0.7459612   0.5479498
## wrd_length                                 PA1 -0.7287759   0.8014540
## pp_demonstrative                           PA1  0.6948488   0.5562340
## nn_all                                     PA1 -0.6544929   0.8300732
## pp3_it                                     PA1  0.6386236   0.4259388
## pp1                                        PA1  0.6247616   0.4914034
## mean_verbal_deps                           PA1 -0.5596111   0.4105018
## nn_abstract                                PA1 -0.5310264   0.3650471
## nominalization                             PA1 -0.5275952   0.4631378
## factive_adverb                             PA1  0.5214767   0.3249478
## mattr                                      PA1 -0.5053301   0.2980419
## mlc                                        PA1 -0.4958467   0.6864842
## det_nominal                                PA1  0.4191834   0.3575159
## to_clause_noun                             PA1 -0.4157106   0.2123137
## prep_phrase                                PA1 -0.3286525   0.3639326
## adverbial_subordinator_causitive           PA1  0.3261532   0.1637591
## pp_indefinite                              PA1  0.3241266   0.1685381
## discourse_particle                         PA1  0.3163154   0.2265938
## time_adverbials                            PA1  0.3125315   0.2088412

## 
## 
##                         factor_loaded   loading communality
## relcl_c                           PA2 0.8461425   0.7192315
## wh_relative_clause                PA2 0.7474298   0.7072877
## wh_relative_subj_clause           PA2 0.6364631   0.5601752
## relcl_nominal                     PA2 0.6262247   0.7207488
## dc_c                              PA2 0.4661064   0.5837262
## that_relative_clause              PA2 0.3770284   0.3211368

## 
## 
##                       factor_loaded    loading communality
## verb                            PA3  0.8366189   0.8913202
## to_clause                       PA3  0.7636545   0.5577178
## non_past_tense                  PA3  0.7048001   0.8220477
## to_clause_verb                  PA3  0.5073746   0.4021488
## activity_verb                   PA3  0.4300264   0.3092743
## modal_possibility               PA3  0.4107499   0.1870198
## to_clause_verb_desire           PA3  0.4047185   0.2393614
## pp2                             PA3  0.3903002   0.3594773
## mental_verb                     PA3  0.3888151   0.3958738
## mean_nominal_deps               PA3 -0.3553521   0.2337824
## prep_nominal                    PA3 -0.3286380   0.2553694

## 
## 
##                          factor_loaded   loading communality
## that_verb_clause                   PA4 0.9243763   0.7824317
## that_complement_clause             PA4 0.9231932   0.8495170
## that_verb_clause_factive           PA4 0.7799246   0.5533175
## ccomp_c                            PA4 0.5438557   0.4391453

## 
## 
##              factor_loaded   loading communality
## pp3                    PA5 0.6636445   0.4447154
## past_tense             PA5 0.5888479   0.4128245
## nn_animate             PA5 0.5617818   0.3306635
## poss_nominal           PA5 0.4224054   0.3220701

## 
## 
##            factor_loaded   loading communality
## cc_nominal           PA6 0.4683098   0.2792157
## cc_phrase            PA6 0.4272668   0.4114733

## 
## 
##                   factor_loaded    loading communality
## jj_attributive              PA7  0.6090223   0.5425127
## amod_nominal                PA7  0.5180351   0.2752260
## mltu                        PA7 -0.5138994   0.5736179
## agentless_passive           PA7  0.3684972   0.2165383

2.4.2 Dimension score: Discipline by mode

MD.visualize(PA7, PA7_reduced, btr_filtered, x = discipline, color = discipline, grid = mode~learning_environment, legend = F)
##                                  factor_loaded    loading communality
## nonfinite_prop                             PA1 -0.8235353   0.7244426
## contraction                                PA1  0.8186383   0.6431457
## cc_clause                                  PA1  0.7961354   0.6064587
## emphatics                                  PA1  0.7795884   0.5482211
## be_mv                                      PA1  0.7459612   0.5479498
## wrd_length                                 PA1 -0.7287759   0.8014540
## pp_demonstrative                           PA1  0.6948488   0.5562340
## nn_all                                     PA1 -0.6544929   0.8300732
## pp3_it                                     PA1  0.6386236   0.4259388
## pp1                                        PA1  0.6247616   0.4914034
## mean_verbal_deps                           PA1 -0.5596111   0.4105018
## nn_abstract                                PA1 -0.5310264   0.3650471
## nominalization                             PA1 -0.5275952   0.4631378
## factive_adverb                             PA1  0.5214767   0.3249478
## mattr                                      PA1 -0.5053301   0.2980419
## mlc                                        PA1 -0.4958467   0.6864842
## det_nominal                                PA1  0.4191834   0.3575159
## to_clause_noun                             PA1 -0.4157106   0.2123137
## prep_phrase                                PA1 -0.3286525   0.3639326
## adverbial_subordinator_causitive           PA1  0.3261532   0.1637591
## pp_indefinite                              PA1  0.3241266   0.1685381
## discourse_particle                         PA1  0.3163154   0.2265938
## time_adverbials                            PA1  0.3125315   0.2088412

## 
## 
##                         factor_loaded   loading communality
## relcl_c                           PA2 0.8461425   0.7192315
## wh_relative_clause                PA2 0.7474298   0.7072877
## wh_relative_subj_clause           PA2 0.6364631   0.5601752
## relcl_nominal                     PA2 0.6262247   0.7207488
## dc_c                              PA2 0.4661064   0.5837262
## that_relative_clause              PA2 0.3770284   0.3211368

## 
## 
##                       factor_loaded    loading communality
## verb                            PA3  0.8366189   0.8913202
## to_clause                       PA3  0.7636545   0.5577178
## non_past_tense                  PA3  0.7048001   0.8220477
## to_clause_verb                  PA3  0.5073746   0.4021488
## activity_verb                   PA3  0.4300264   0.3092743
## modal_possibility               PA3  0.4107499   0.1870198
## to_clause_verb_desire           PA3  0.4047185   0.2393614
## pp2                             PA3  0.3903002   0.3594773
## mental_verb                     PA3  0.3888151   0.3958738
## mean_nominal_deps               PA3 -0.3553521   0.2337824
## prep_nominal                    PA3 -0.3286380   0.2553694

## 
## 
##                          factor_loaded   loading communality
## that_verb_clause                   PA4 0.9243763   0.7824317
## that_complement_clause             PA4 0.9231932   0.8495170
## that_verb_clause_factive           PA4 0.7799246   0.5533175
## ccomp_c                            PA4 0.5438557   0.4391453

## 
## 
##              factor_loaded   loading communality
## pp3                    PA5 0.6636445   0.4447154
## past_tense             PA5 0.5888479   0.4128245
## nn_animate             PA5 0.5617818   0.3306635
## poss_nominal           PA5 0.4224054   0.3220701

## 
## 
##            factor_loaded   loading communality
## cc_nominal           PA6 0.4683098   0.2792157
## cc_phrase            PA6 0.4272668   0.4114733

## 
## 
##                   factor_loaded    loading communality
## jj_attributive              PA7  0.6090223   0.5425127
## amod_nominal                PA7  0.5180351   0.2752260
## mltu                        PA7 -0.5138994   0.5736179
## agentless_passive           PA7  0.3684972   0.2165383

2.5 Eight-factor solution (Heywood case produced)

New analysis: Heywood cases. - PA with 8 factors explained 35% of the total variance - The root mean square of the residuals (RMSR) is 0.04 - The df corrected root mean square of the residuals is 0.04 - Tucker Lewis Index of factoring reliability = 0.521 - RMSEA index = 0.079 and the 95 % confidence intervals are 0.078 0.079

PA8 <- fa(btr_filtered[8:n_col],nfactors= 8, n.iter=1, rotate="Promax",  
          residuals=TRUE, SMC=TRUE, missing=TRUE,impute="median",
          min.err = 0.001,  max.iter = 50, symmetric=TRUE, warnings=TRUE, fm="pa",
          alpha=.05, p=.05, oblique.scores=TRUE)
#PA8 <- fa.sort(PA8, polar = FALSE)
#print(PA8)
#PA8_reduced <- MD.reduce(PA8, .3, .15)
#paged_table(PA8_reduced)
#PA8_reduced %>%
 # na.omit() %>%
  #dim()
#PA8_scores <- MD.scores(PA8, PA8_reduced, btr_filtered)
#paged_table(PA8_scores)

2.6 Summary of MDA

  • Biber et al. (2004) reported that their 4-factor solution accounted for 46.9% of the variance in T2K-SWAL corpus.
  • However, in this new MDA, the scree plot continues to drop until 6-factor solution, then started to flattens out. This means that Four-factor and five factor solutions may be underfactoring.
  • When Scree plot is examined again, which suggests that we could extract from 6 or 7 (see the plot reproduced below).
plot(screeplot)

  • Increasing the factor does not appear to solve the issue of explained variance (see Table below).
data.frame(VarianceExplained = c(PA6$Vaccounted[3,6], PA7$Vaccounted[3,7], PA8$Vaccounted[3,8]), row.names = c('6-factor', '7-factor', '8-factor'))
  • Then it may be the case that the corpus has a lot of new small variabilities and the dimensions are more fine-grained (up to 7 factor would be possible with Promax rotation).
  • We concluded that 6-factor solution was optimal.

2.7 Dimension score: Discipline by mode

MD.visualize(PA6, PA6_refined, btr_filtered, x = discipline, color = discipline, grid = mode~learning_environment, legend = F)
##                                  factor_loaded    loading communality
## nonfinite_prop                             PA1 -0.8132950   0.7253723
## cc_clause                                  PA1  0.7742338   0.5873482
## contraction                                PA1  0.7542997   0.6407286
## emphatics                                  PA1  0.7495712   0.5427764
## be_mv                                      PA1  0.7322963   0.5486265
## pp_demonstrative                           PA1  0.6335961   0.5565309
## nn_all                                     PA1 -0.6188822   0.8163238
## pp1                                        PA1  0.6131749   0.4810347
## pp3_it                                     PA1  0.5852639   0.4258781
## mean_verbal_deps                           PA1 -0.5684253   0.3699637
## factive_adverb                             PA1  0.5566128   0.3073590
## mlc                                        PA1 -0.4843049   0.5393716
## nn_abstract                                PA1 -0.4419376   0.3660817
## mltu                                       PA1 -0.4329341   0.3313769
## mattr                                      PA1 -0.4105833   0.2990983
## that_relative_clause                       PA1  0.3478179   0.3277435
## det_nominal                                PA1  0.3413118   0.3559768
## adverbial_subordinator_causitive           PA1  0.3082464   0.1632622

## 
## 
##                                    factor_loaded    loading communality
## wrd_length                                   PA2  0.5880763   0.7977080
## cc_phrase                                    PA2  0.5613025   0.4137876
## cc_nominal                                   PA2  0.5260028   0.2808598
## nominalization                               PA2  0.4980983   0.4664133
## jj_attributive                               PA2  0.4408125   0.4063362
## amod_nominal                                 PA2  0.3734195   0.1939523
## prep_phrase                                  PA2  0.3538703   0.3669912
## adverbial_subordinator_conditional           PA2 -0.3301145   0.1789314
## prep_nominal                                 PA2  0.3007542   0.2453117

## 
## 
##                       factor_loaded   loading communality
## to_clause                       PA3 0.8032894   0.5551529
## verb                            PA3 0.6946974   0.7949891
## non_past_tense                  PA3 0.6651300   0.7967250
## to_clause_verb                  PA3 0.5146971   0.3993298
## pp2                             PA3 0.5144921   0.3540182
## mental_verb                     PA3 0.5047000   0.3849005
## to_clause_verb_desire           PA3 0.4472352   0.2408542
## to_clause_noun                  PA3 0.4389121   0.2124316
## dc_c                            PA3 0.4125804   0.5075388
## activity_verb                   PA3 0.3906763   0.2960751
## poss_nominal                    PA3 0.3721704   0.2740686

## 
## 
##                          factor_loaded   loading communality
## that_verb_clause                   PA4 0.8855422   0.7373146
## that_complement_clause             PA4 0.8385487   0.7637723
## that_verb_clause_factive           PA4 0.7869121   0.5464035
## ccomp_c                            PA4 0.4685438   0.3955203

## 
## 
##            factor_loaded   loading communality
## pp3                  PA5 0.6110859   0.4314571
## past_tense           PA5 0.5767110   0.4043281
## nn_animate           PA5 0.4394147   0.2878991

## 
## 
##                         factor_loaded   loading communality
## wh_relative_clause                PA6 0.8238232   0.7104070
## relcl_c                           PA6 0.8071224   0.6962011
## wh_relative_subj_clause           PA6 0.7076527   0.5628490
## relcl_nominal                     PA6 0.5960646   0.7154814

3 Regression models

RQ1) What are the linguistic features of technology-mediated learning environments that are encountered and produced?

RQ2) How (dis)similar are the linguistic features of technology-mediated learning environments and the academic registers represented by T2K-SWAL with regard to mode, register, and discipline??

  • Dimensional_score ~ Dimension * learning_environment * (mode + discipline + text_type) + (1+Dimension|filename) + (1+mode+|discipline) + (1|text_type)
head(PA6_scores)

3.1 Descriptive stats of dimensional scores

describeBy(PA6_scores, group = learning_environment)
## 
##  Descriptive statistics by group 
## group: tmle
##                       vars    n    mean      sd  median trimmed     mad    min
## filename*                1 4101 2051.00 1184.00 2051.00 2051.00 1519.66   1.00
## learning_environment*    2 4101    1.00    0.00    1.00    1.00    0.00   1.00
## mode*                    3 4101    1.46    0.50    1.00    1.44    0.00   1.00
## discipline*              4 4101    3.25    1.83    3.00    3.19    2.97   1.00
## subdiscipline*           5 4070  284.24  137.12  308.00  285.17  148.26   1.00
## text_type*               6 4101    4.52    1.61    5.00    4.59    0.00   1.00
## PA1                      7 4101   -0.68   11.12   -0.80   -0.51   12.73 -51.34
## PA3                      8 4101    0.02    6.65   -0.01   -0.06    6.40 -21.27
## PA4                      9 4101    0.04    3.49   -0.58   -0.36    2.99  -4.33
## PA2                     10 4101    0.17    5.29   -0.17    0.03    4.99 -23.30
## PA6                     11 4101    0.04    3.25   -0.32   -0.22    2.85  -4.56
## PA5                     12 4101   -0.07    2.23   -0.66   -0.37    1.94  -2.64
##                           max   range  skew kurtosis    se
## filename*             4101.00 4100.00  0.00    -1.20 18.49
## learning_environment*    1.00    0.00   NaN      NaN  0.00
## mode*                    2.00    1.00  0.18    -1.97  0.01
## discipline*              6.00    5.00  0.16    -1.36  0.03
## subdiscipline*         562.00  561.00 -0.07    -0.93  2.15
## text_type*               8.00    7.00 -0.54     0.55  0.03
## PA1                     31.68   83.02 -0.19    -0.22  0.17
## PA3                     34.01   55.28  0.20     0.60  0.10
## PA4                     30.39   34.72  1.57     4.64  0.05
## PA2                     23.90   47.21  0.22     0.67  0.08
## PA6                     31.52   36.08  1.53     7.07  0.05
## PA5                     14.02   16.66  1.36     2.32  0.03
## ------------------------------------------------------------ 
## group: traditional
##                       vars   n   mean     sd median trimmed    mad    min
## filename*                1 462 231.50 133.51 231.50  231.50 171.24   1.00
## learning_environment*    2 462   1.00   0.00   1.00    1.00   0.00   1.00
## mode*                    3 462   1.37   0.48   1.00    1.34   0.00   1.00
## discipline*              4 462   4.45   2.37   4.00    4.44   2.97   1.00
## subdiscipline*           5 462  37.22  22.65  38.00   36.96  32.62   1.00
## text_type*               6 462   5.87   2.76   5.00    5.94   2.97   1.00
## PA1                      7 462   6.01  11.80  10.12    6.48  11.48 -57.92
## PA3                      8 462  -0.19   4.96   0.18   -0.04   5.05 -13.32
## PA4                      9 462  -0.37   1.59  -0.38   -0.40   1.54  -3.86
## PA2                     10 462  -1.49   6.82  -3.47   -1.92   6.72 -13.05
## PA6                     11 462  -0.32   1.58  -0.47   -0.36   1.56  -4.56
## PA5                     12 462   0.63   1.85   0.37    0.47   1.89  -2.45
##                          max  range  skew kurtosis   se
## filename*             462.00 461.00  0.00    -1.21 6.21
## learning_environment*   1.00   0.00   NaN      NaN 0.00
## mode*                   2.00   1.00  0.53    -1.73 0.02
## discipline*             8.00   7.00  0.14    -1.15 0.11
## subdiscipline*         75.00  74.00  0.05    -1.31 1.05
## text_type*             10.00   9.00  0.08    -0.89 0.13
## PA1                    27.63  85.55 -0.62     0.32 0.55
## PA3                    15.23  28.55 -0.22    -0.33 0.23
## PA4                     4.32   8.17  0.19    -0.25 0.07
## PA2                    15.62  28.67  0.53    -0.76 0.32
## PA6                     6.17  10.72  0.37     0.45 0.07
## PA5                     9.29  11.75  0.92     1.27 0.09

3.2 Data wrangling

3.2.1 transforming the dataset to long format

data_lengths <- dim(PA6_scores)

PA6_scores2 <- PA6_scores

for (r in 1:data_lengths[1]){
  if (PA6_scores2$text_type[r] == "classroom_management_talk" & PA6_scores2$learning_environment[r] == "tmle") {
    PA6_scores2$text_type[r] <- "classroom_management_talk_tmle"
  }
  if (PA6_scores2$text_type[r] == "classroom_management_talk" & PA6_scores2$learning_environment[r] == "traditional") {
    PA6_scores2$text_type[r] <- "classroom_management_talk_trad"
  }
}

unique(PA6_scores2$text_type)
##  [1] "instructional_reading"          "announcements_discussions"     
##  [3] "assignment_description"         "instructional_video"           
##  [5] "slides"                         "syllabus"                      
##  [7] "quiz"                           "classroom_management_talk_tmle"
##  [9] "textbooks"                      "course_packs"                  
## [11] "course_management"              "other_institutional_writing"   
## [13] "lecture"                        "service_encounter"             
## [15] "study_group"                    "lab"                           
## [17] "classroom_management_talk_trad" "office_hours"
attach(PA6_scores2)
head(PA6_scores2) 

3.3 Setting reference category

PA6_scores2$text_type <- factor(PA6_scores2$text_type)
PA6_scores2$text_type <- factor(PA6_scores2$text_type, levels = rev(c("classroom_management_talk_tmle", "instructional_video",
                                                                  "classroom_management_talk_trad", "lab", "lecture"
,"office_hours"
,"service_encounter"
,"study_group"
,"announcements_discussions"
,"assignment_description"
,"instructional_reading"
,"quiz"
,"slides"
,"syllabus"
,"course_management"
,"course_packs"
,"other_institutional_writing"
,"textbooks")))

contrasts(PA6_scores2$text_type)
##                                other_institutional_writing course_packs
## textbooks                                                0            0
## other_institutional_writing                              1            0
## course_packs                                             0            1
## course_management                                        0            0
## syllabus                                                 0            0
## slides                                                   0            0
## quiz                                                     0            0
## instructional_reading                                    0            0
## assignment_description                                   0            0
## announcements_discussions                                0            0
## study_group                                              0            0
## service_encounter                                        0            0
## office_hours                                             0            0
## lecture                                                  0            0
## lab                                                      0            0
## classroom_management_talk_trad                           0            0
## instructional_video                                      0            0
## classroom_management_talk_tmle                           0            0
##                                course_management syllabus slides quiz
## textbooks                                      0        0      0    0
## other_institutional_writing                    0        0      0    0
## course_packs                                   0        0      0    0
## course_management                              1        0      0    0
## syllabus                                       0        1      0    0
## slides                                         0        0      1    0
## quiz                                           0        0      0    1
## instructional_reading                          0        0      0    0
## assignment_description                         0        0      0    0
## announcements_discussions                      0        0      0    0
## study_group                                    0        0      0    0
## service_encounter                              0        0      0    0
## office_hours                                   0        0      0    0
## lecture                                        0        0      0    0
## lab                                            0        0      0    0
## classroom_management_talk_trad                 0        0      0    0
## instructional_video                            0        0      0    0
## classroom_management_talk_tmle                 0        0      0    0
##                                instructional_reading assignment_description
## textbooks                                          0                      0
## other_institutional_writing                        0                      0
## course_packs                                       0                      0
## course_management                                  0                      0
## syllabus                                           0                      0
## slides                                             0                      0
## quiz                                               0                      0
## instructional_reading                              1                      0
## assignment_description                             0                      1
## announcements_discussions                          0                      0
## study_group                                        0                      0
## service_encounter                                  0                      0
## office_hours                                       0                      0
## lecture                                            0                      0
## lab                                                0                      0
## classroom_management_talk_trad                     0                      0
## instructional_video                                0                      0
## classroom_management_talk_tmle                     0                      0
##                                announcements_discussions study_group
## textbooks                                              0           0
## other_institutional_writing                            0           0
## course_packs                                           0           0
## course_management                                      0           0
## syllabus                                               0           0
## slides                                                 0           0
## quiz                                                   0           0
## instructional_reading                                  0           0
## assignment_description                                 0           0
## announcements_discussions                              1           0
## study_group                                            0           1
## service_encounter                                      0           0
## office_hours                                           0           0
## lecture                                                0           0
## lab                                                    0           0
## classroom_management_talk_trad                         0           0
## instructional_video                                    0           0
## classroom_management_talk_tmle                         0           0
##                                service_encounter office_hours lecture lab
## textbooks                                      0            0       0   0
## other_institutional_writing                    0            0       0   0
## course_packs                                   0            0       0   0
## course_management                              0            0       0   0
## syllabus                                       0            0       0   0
## slides                                         0            0       0   0
## quiz                                           0            0       0   0
## instructional_reading                          0            0       0   0
## assignment_description                         0            0       0   0
## announcements_discussions                      0            0       0   0
## study_group                                    0            0       0   0
## service_encounter                              1            0       0   0
## office_hours                                   0            1       0   0
## lecture                                        0            0       1   0
## lab                                            0            0       0   1
## classroom_management_talk_trad                 0            0       0   0
## instructional_video                            0            0       0   0
## classroom_management_talk_tmle                 0            0       0   0
##                                classroom_management_talk_trad
## textbooks                                                   0
## other_institutional_writing                                 0
## course_packs                                                0
## course_management                                           0
## syllabus                                                    0
## slides                                                      0
## quiz                                                        0
## instructional_reading                                       0
## assignment_description                                      0
## announcements_discussions                                   0
## study_group                                                 0
## service_encounter                                           0
## office_hours                                                0
## lecture                                                     0
## lab                                                         0
## classroom_management_talk_trad                              1
## instructional_video                                         0
## classroom_management_talk_tmle                              0
##                                instructional_video
## textbooks                                        0
## other_institutional_writing                      0
## course_packs                                     0
## course_management                                0
## syllabus                                         0
## slides                                           0
## quiz                                             0
## instructional_reading                            0
## assignment_description                           0
## announcements_discussions                        0
## study_group                                      0
## service_encounter                                0
## office_hours                                     0
## lecture                                          0
## lab                                              0
## classroom_management_talk_trad                   0
## instructional_video                              1
## classroom_management_talk_tmle                   0
##                                classroom_management_talk_tmle
## textbooks                                                   0
## other_institutional_writing                                 0
## course_packs                                                0
## course_management                                           0
## syllabus                                                    0
## slides                                                      0
## quiz                                                        0
## instructional_reading                                       0
## assignment_description                                      0
## announcements_discussions                                   0
## study_group                                                 0
## service_encounter                                           0
## office_hours                                                0
## lecture                                                     0
## lab                                                         0
## classroom_management_talk_trad                              0
## instructional_video                                         0
## classroom_management_talk_tmle                              1
head(PA6_scores2)

3.4 Making the data longer

dim_score_long <- reshape2::melt(PA6_scores2, ids = c("filename", "learning_environment", "mode", 
                                           "discipline", "subdiscipline", "text_type"),
                       value.name = "Dimensional_score",
                       variable.name = "Dimension")
head(dim_score_long)
dim_score_long$Dimension <- gsub("PA", 'DIM', dim_score_long$Dimension)


dim_score_long$Dimension <- factor(dim_score_long$Dimension, levels = c("DIM1", "DIM2", "DIM3", "DIM4", "DIM5", "DIM6"))
contrasts(dim_score_long$Dimension)
##      DIM2 DIM3 DIM4 DIM5 DIM6
## DIM1    0    0    0    0    0
## DIM2    1    0    0    0    0
## DIM3    0    1    0    0    0
## DIM4    0    0    1    0    0
## DIM5    0    0    0    1    0
## DIM6    0    0    0    0    1
contrasts(dim_score_long$text_type)
##                                other_institutional_writing course_packs
## textbooks                                                0            0
## other_institutional_writing                              1            0
## course_packs                                             0            1
## course_management                                        0            0
## syllabus                                                 0            0
## slides                                                   0            0
## quiz                                                     0            0
## instructional_reading                                    0            0
## assignment_description                                   0            0
## announcements_discussions                                0            0
## study_group                                              0            0
## service_encounter                                        0            0
## office_hours                                             0            0
## lecture                                                  0            0
## lab                                                      0            0
## classroom_management_talk_trad                           0            0
## instructional_video                                      0            0
## classroom_management_talk_tmle                           0            0
##                                course_management syllabus slides quiz
## textbooks                                      0        0      0    0
## other_institutional_writing                    0        0      0    0
## course_packs                                   0        0      0    0
## course_management                              1        0      0    0
## syllabus                                       0        1      0    0
## slides                                         0        0      1    0
## quiz                                           0        0      0    1
## instructional_reading                          0        0      0    0
## assignment_description                         0        0      0    0
## announcements_discussions                      0        0      0    0
## study_group                                    0        0      0    0
## service_encounter                              0        0      0    0
## office_hours                                   0        0      0    0
## lecture                                        0        0      0    0
## lab                                            0        0      0    0
## classroom_management_talk_trad                 0        0      0    0
## instructional_video                            0        0      0    0
## classroom_management_talk_tmle                 0        0      0    0
##                                instructional_reading assignment_description
## textbooks                                          0                      0
## other_institutional_writing                        0                      0
## course_packs                                       0                      0
## course_management                                  0                      0
## syllabus                                           0                      0
## slides                                             0                      0
## quiz                                               0                      0
## instructional_reading                              1                      0
## assignment_description                             0                      1
## announcements_discussions                          0                      0
## study_group                                        0                      0
## service_encounter                                  0                      0
## office_hours                                       0                      0
## lecture                                            0                      0
## lab                                                0                      0
## classroom_management_talk_trad                     0                      0
## instructional_video                                0                      0
## classroom_management_talk_tmle                     0                      0
##                                announcements_discussions study_group
## textbooks                                              0           0
## other_institutional_writing                            0           0
## course_packs                                           0           0
## course_management                                      0           0
## syllabus                                               0           0
## slides                                                 0           0
## quiz                                                   0           0
## instructional_reading                                  0           0
## assignment_description                                 0           0
## announcements_discussions                              1           0
## study_group                                            0           1
## service_encounter                                      0           0
## office_hours                                           0           0
## lecture                                                0           0
## lab                                                    0           0
## classroom_management_talk_trad                         0           0
## instructional_video                                    0           0
## classroom_management_talk_tmle                         0           0
##                                service_encounter office_hours lecture lab
## textbooks                                      0            0       0   0
## other_institutional_writing                    0            0       0   0
## course_packs                                   0            0       0   0
## course_management                              0            0       0   0
## syllabus                                       0            0       0   0
## slides                                         0            0       0   0
## quiz                                           0            0       0   0
## instructional_reading                          0            0       0   0
## assignment_description                         0            0       0   0
## announcements_discussions                      0            0       0   0
## study_group                                    0            0       0   0
## service_encounter                              1            0       0   0
## office_hours                                   0            1       0   0
## lecture                                        0            0       1   0
## lab                                            0            0       0   1
## classroom_management_talk_trad                 0            0       0   0
## instructional_video                            0            0       0   0
## classroom_management_talk_tmle                 0            0       0   0
##                                classroom_management_talk_trad
## textbooks                                                   0
## other_institutional_writing                                 0
## course_packs                                                0
## course_management                                           0
## syllabus                                                    0
## slides                                                      0
## quiz                                                        0
## instructional_reading                                       0
## assignment_description                                      0
## announcements_discussions                                   0
## study_group                                                 0
## service_encounter                                           0
## office_hours                                                0
## lecture                                                     0
## lab                                                         0
## classroom_management_talk_trad                              1
## instructional_video                                         0
## classroom_management_talk_tmle                              0
##                                instructional_video
## textbooks                                        0
## other_institutional_writing                      0
## course_packs                                     0
## course_management                                0
## syllabus                                         0
## slides                                           0
## quiz                                             0
## instructional_reading                            0
## assignment_description                           0
## announcements_discussions                        0
## study_group                                      0
## service_encounter                                0
## office_hours                                     0
## lecture                                          0
## lab                                              0
## classroom_management_talk_trad                   0
## instructional_video                              1
## classroom_management_talk_tmle                   0
##                                classroom_management_talk_tmle
## textbooks                                                   0
## other_institutional_writing                                 0
## course_packs                                                0
## course_management                                           0
## syllabus                                                    0
## slides                                                      0
## quiz                                                        0
## instructional_reading                                       0
## assignment_description                                      0
## announcements_discussions                                   0
## study_group                                                 0
## service_encounter                                           0
## office_hours                                                0
## lecture                                                     0
## lab                                                         0
## classroom_management_talk_trad                              0
## instructional_video                                         0
## classroom_management_talk_tmle                              1
xtabs(~text_type+learning_environment)
##                                 learning_environment
## text_type                        tmle traditional
##   announcements_discussions       326           0
##   assignment_description          355           0
##   classroom_management_talk_tmle   26           0
##   classroom_management_talk_trad    0          38
##   course_management                 0          21
##   course_packs                      0          27
##   instructional_reading           624           0
##   instructional_video            2207           0
##   lab                               0          17
##   lecture                           0         177
##   office_hours                      0          11
##   other_institutional_writing       0          37
##   quiz                            246           0
##   service_encounter                 0          22
##   slides                          138           0
##   study_group                       0          25
##   syllabus                        179           0
##   textbooks                         0          87
attach(dim_score_long)
xtabs(~discipline+learning_environment)
##                     learning_environment
## discipline           tmle traditional
##   business           6732         420
##   education          2796         246
##   engineering        4224         360
##   humanities         3510         450
##   natural_science    3018         444
##   other                 0         180
##   service_encounters    0         132
##   social_science     4326         540

3.5 General modeling strategy

3.5.1 Fixed-effects

We are interested in examining the differences among Dimension, learning environment and one of the three situational variable (text type). - The question is if we center the binary variable of mode. This will make the main effects of learning_environment independent of the mode. This may not affect the results much IF we refer to the esmeans() since it will calculate all the

  1. Maximal model (Barr et al., 2013)
  • Random intercept, slopes, and their interactions, when it converges. This will not converge, but it is important to examine to guard against the type 1 error.

a includes all the parameters (1+dummy(Dimension, “PA1”)|filename) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)|discipline/mode) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)|text_type) -> The correlations between random intercept and slopes are hard to interpret.

  1. Uncorrelated random intercepts and slopes

    (1+dummy(Dimension, “PA1”)||filename) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||discipline/mode) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||text_type) -> This would be the random effect structure what is suitable for current study.

b.1 excludes the intercepts for filename (0+dummy(Dimension, “PA1”)||filename) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||discipline/mode) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||text_type)

  1. No random slopes
  • c.1 removes slope for filename (1|filename) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||discipline:mode) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||text_type)

  • c.2 removes slope for discipline:mode (1+dummy(Dimension, “PA1”)||filename) + (1|discipline:mode) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||text_type)

  • c.3 removes slope for text_type (1+dummy(Dimension, “PA1”)||filename) + (1+dummy(Dimension,“PA1”)+dummy(learning_environment,“tmle”)||discipline:mode) + (1|text_type)

  • c.12, c.23, c.123 is also possible

3.6 Comparisons of Register (Text type)

3.6.1 Model building

#baseline model 
baseline_1.a <- lmer(Dimensional_score ~ Dimension +  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))


text_type_1.b <- lmer(Dimensional_score ~ Dimension * text_type +  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||discipline/mode) +
        (1+dummy(Dimension,"DIM1")||filename:text_type)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

lmer(Score ~ text_type + (1 + text_type | discipline/mode) + + (1 | text_type))

lmer(Score ~ Dimension * text_type + (0 + Dimension | filename) + (1 + Dimension | text_type) + (1 + Dimension * text_type | discipline/mode) +

3.7 Model fit indices

anova(baseline_1.a, text_type_1.b)
rbind(BIC(baseline_1.a), BIC(text_type_1.b))
##          [,1]
## [1,] 163334.9
## [2,] 159796.7
BIC(baseline_1.a) - BIC(text_type_1.b)
## [1] 3538.212
rbind(AIC(baseline_1.a), AIC(text_type_1.b))
##          [,1]
## [1,] 163187.0
## [2,] 158810.6
AIC(baseline_1.a) - AIC(text_type_1.b)
## [1] 4376.396

3.7.1 Model R^2

MuMIn::r.squaredGLMM(baseline_1.a)
##              R2m       R2c
## [1,] 0.002873728 0.5503542
MuMIn::r.squaredGLMM(text_type_1.b)
##            R2m       R2c
## [1,] 0.4104583 0.5839392

3.7.2 Other non-converging /non- final random structure tested.

  • These are different random effects structure tried, which was not used in the final manuscript.
text_type_1.a <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a)
#anova(baseline_1.a, text_type_1.a)


text_type_1.a2 <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

# This is identical with our model 
text_type_1.a_mv <- lmer(Dimensional_score ~ 0 + Dimension:text_type + text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

# did not converge
text_type_1.a_mv2 <- lmer(Dimensional_score ~ 0 + Dimension:text_type +  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))



# having the main effect did not converge
text_type_1.a_mv3 <- lmer(Dimensional_score ~ 0 + Dimension:text_type + Dimension +  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

# turned out that we do not need intercepts for filename
text_type_1.a_mv4 <- lmer(Dimensional_score ~ 0+Dimension*text_type +  
        (1|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

# Then did not convverge 
text_type_1.a_mv5 <- lmer(Dimensional_score ~ 0+Dimension*text_type +  
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

text_type_1.a_no_cor <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

text_type_1.a_no_texttype_random <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

text_type_1.a_no_cor <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")+dummy(text_type,"lecture")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a_no_cor)

##
text_type_1.a4 <- lmer(Dimensional_score ~ Dimension * text_type +  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||discipline/mode) +
        (1+dummy(Dimension,"DIM1")||text_type)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a4)

text_type_1.a5 <- lmer(Dimensional_score ~ Dimension * text_type +
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||filename:discipline) +
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||filename:mode) +
        (1+dummy(Dimension,"DIM1")||filename:text_type)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a5)

text_type_1.a6 <- lmer(Dimensional_score ~ Dimension * text_type +
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||filename:discipline) +
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||filename:mode) +
        (1+dummy(Dimension,"DIM1")||filename:text_type)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a6)

text_type_1.a7 <- lmer(Dimensional_score ~ Dimension * text_type +
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||discipline) +
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"lecture")||mode) +
        (1+dummy(Dimension,"DIM1")||text_type)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a7)



text_type_1.a2 <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"instructional_video")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))

#summary(text_type_1.a2)
#contrasts(dim_score_long$text_type)

# This converged, although having singular fit
text_type_1.a1 <- lmer(Dimensional_score ~ Dimension * text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"instructional_reading")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a1)

#perfect collinear 
text_type_1.a2 <- lmer(Dimensional_score ~ Dimension * learning_environment *text_type+  
        (0+dummy(Dimension, "DIM1")|filename) + 
        (1+dummy(Dimension,"DIM1")*dummy(text_type,"instructional_reading")|discipline:mode)
        , REML = F,data = dim_score_long,
        lmerControl(optimizer="bobyqa"))
#summary(text_type_1.a2)

3.7.3 Final model for Register (text type)

summary(text_type_1.b)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: Dimensional_score ~ Dimension * text_type + (0 + dummy(Dimension,  
##     "DIM1") | filename) + (1 + dummy(Dimension, "DIM1") * dummy(text_type,  
##     "lecture") || discipline/mode) + (1 + dummy(Dimension, "DIM1") ||  
##     filename:text_type)
##    Data: dim_score_long
## Control: lmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
## 158810.6 159796.7 -79285.3 158570.6    27258 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.8880 -0.4834 -0.0734  0.4087  8.7108 
## 
## Random effects:
##  Groups               Name                                                
##  filename.text_type   dummy(Dimension, "DIM1")                            
##  filename.text_type.1 (Intercept)                                         
##  filename             dummy(Dimension, "DIM1")                            
##  mode.discipline      dummy(Dimension, "DIM1"):dummy(text_type, "lecture")
##  mode.discipline.1    dummy(text_type, "lecture")                         
##  mode.discipline.2    dummy(Dimension, "DIM1")                            
##  mode.discipline.3    (Intercept)                                         
##  discipline           dummy(Dimension, "DIM1"):dummy(text_type, "lecture")
##  discipline.1         dummy(text_type, "lecture")                         
##  discipline.2         dummy(Dimension, "DIM1")                            
##  discipline.3         (Intercept)                                         
##  Residual                                                                 
##  Variance  Std.Dev. 
##  3.251e+01 5.701e+00
##  2.314e-13 4.811e-07
##  1.548e+00 1.244e+00
##  1.681e+00 1.297e+00
##  0.000e+00 0.000e+00
##  2.750e+00 1.658e+00
##  1.216e-02 1.103e-01
##  2.368e+00 1.539e+00
##  0.000e+00 0.000e+00
##  8.229e-01 9.071e-01
##  2.750e-01 5.244e-01
##  1.579e+01 3.974e+00
## Number of obs: 27378, groups:  
## filename:text_type, 4563; filename, 4563; mode:discipline, 14; discipline, 8
## 
## Fixed effects:
##                                                         Estimate Std. Error
## (Intercept)                                             -6.55600    1.09190
## DimensionDIM2                                           13.10668    1.15389
## DimensionDIM3                                            0.79814    1.15389
## DimensionDIM4                                            5.99647    1.15389
## DimensionDIM5                                            7.09268    1.15389
## DimensionDIM6                                            6.61024    1.15389
## text_typeother_institutional_writing                    -5.72949    1.97301
## text_typecourse_packs                                   -0.62854    1.55809
## text_typecourse_management                              -1.05864    1.72335
## text_typesyllabus                                       -8.69029    0.92937
## text_typeslides                                         -5.44901    0.97844
## text_typequiz                                           -2.68726    0.89219
## text_typeinstructional_reading                          -1.49147    0.81271
## text_typeassignment_description                         -2.59643    0.85172
## text_typeannouncements_discussions                      -1.06433    0.85437
## text_typestudy_group                                    25.85581    1.86585
## text_typeservice_encounter                              25.02460    2.70526
## text_typeoffice_hours                                   25.42457    2.45656
## text_typelecture                                        19.22498    1.56829
## text_typelab                                            24.35564    2.11142
## text_typeclassroom_management_talk_trad                 19.96889    1.67266
## text_typeinstructional_video                            13.35260    1.22932
## text_typeclassroom_management_talk_tmle                 10.53853    1.84956
## DimensionDIM2:text_typeother_institutional_writing       6.19305    2.10988
## DimensionDIM3:text_typeother_institutional_writing       6.18622    2.10988
## DimensionDIM4:text_typeother_institutional_writing       3.55918    2.10988
## DimensionDIM5:text_typeother_institutional_writing       5.69189    2.10988
## DimensionDIM6:text_typeother_institutional_writing       5.20469    2.10988
## DimensionDIM2:text_typecourse_packs                      0.36516    1.78730
## DimensionDIM3:text_typecourse_packs                      1.95820    1.78730
## DimensionDIM4:text_typecourse_packs                      0.28261    1.78730
## DimensionDIM5:text_typecourse_packs                      1.54246    1.78730
## DimensionDIM6:text_typecourse_packs                      1.11796    1.78730
## DimensionDIM2:text_typecourse_management                -2.58172    1.97599
## DimensionDIM3:text_typecourse_management                 6.24103    1.97599
## DimensionDIM4:text_typecourse_management                 0.48046    1.97599
## DimensionDIM5:text_typecourse_management                -0.21692    1.97599
## DimensionDIM6:text_typecourse_management                 0.38166    1.97599
## DimensionDIM2:text_typesyllabus                          4.98330    1.06492
## DimensionDIM3:text_typesyllabus                          9.00544    1.06492
## DimensionDIM4:text_typesyllabus                          6.67804    1.06492
## DimensionDIM5:text_typesyllabus                          7.13630    1.06492
## DimensionDIM6:text_typesyllabus                          6.71380    1.06492
## DimensionDIM2:text_typeslides                            1.08539    1.11997
## DimensionDIM3:text_typeslides                            4.04405    1.11997
## DimensionDIM4:text_typeslides                            4.22299    1.11997
## DimensionDIM5:text_typeslides                            4.47978    1.11997
## DimensionDIM6:text_typeslides                            3.84932    1.11997
## DimensionDIM2:text_typequiz                             -3.59774    1.02110
## DimensionDIM3:text_typequiz                              4.99872    1.02110
## DimensionDIM4:text_typequiz                              1.71136    1.02110
## DimensionDIM5:text_typequiz                              1.38591    1.02110
## DimensionDIM6:text_typequiz                              1.97983    1.02110
## DimensionDIM2:text_typeinstructional_reading            -1.57257    0.93148
## DimensionDIM3:text_typeinstructional_reading             4.92103    0.93148
## DimensionDIM4:text_typeinstructional_reading             1.09039    0.93148
## DimensionDIM5:text_typeinstructional_reading             0.78198    0.93148
## DimensionDIM6:text_typeinstructional_reading             1.02422    0.93148
## DimensionDIM2:text_typeassignment_description           -3.92407    0.97569
## DimensionDIM3:text_typeassignment_description            7.40037    0.97569
## DimensionDIM4:text_typeassignment_description            2.00025    0.97569
## DimensionDIM5:text_typeassignment_description            1.64492    0.97569
## DimensionDIM6:text_typeassignment_description            1.53879    0.97569
## DimensionDIM2:text_typeannouncements_discussions        -4.36590    0.97984
## DimensionDIM3:text_typeannouncements_discussions         9.11891    0.97984
## DimensionDIM4:text_typeannouncements_discussions         0.70015    0.97984
## DimensionDIM5:text_typeannouncements_discussions         0.05485    0.97984
## DimensionDIM6:text_typeannouncements_discussions         0.14952    0.97984
## DimensionDIM2:text_typestudy_group                     -40.22041    2.07146
## DimensionDIM3:text_typestudy_group                     -18.68001    2.07146
## DimensionDIM4:text_typestudy_group                     -25.24291    2.07146
## DimensionDIM5:text_typestudy_group                     -24.96414    2.07146
## DimensionDIM6:text_typestudy_group                     -27.65929    2.07146
## DimensionDIM2:text_typeservice_encounter               -42.63832    2.80860
## DimensionDIM3:text_typeservice_encounter               -14.89655    2.80860
## DimensionDIM4:text_typeservice_encounter               -25.89938    2.80860
## DimensionDIM5:text_typeservice_encounter               -24.87655    2.80860
## DimensionDIM6:text_typeservice_encounter               -27.64170    2.80860
## DimensionDIM2:text_typeoffice_hours                    -39.35865    2.76564
## DimensionDIM3:text_typeoffice_hours                    -15.42282    2.76564
## DimensionDIM4:text_typeoffice_hours                    -24.01975    2.76564
## DimensionDIM5:text_typeoffice_hours                    -25.07105    2.76564
## DimensionDIM6:text_typeoffice_hours                    -26.15961    2.76564
## DimensionDIM2:text_typelecture                         -30.37444    1.65126
## DimensionDIM3:text_typelecture                         -11.59768    1.65126
## DimensionDIM4:text_typelecture                         -18.29198    1.65126
## DimensionDIM5:text_typelecture                         -18.95182    1.65126
## DimensionDIM6:text_typelecture                         -19.10567    1.65126
## DimensionDIM2:text_typelab                             -38.20880    2.35937
## DimensionDIM3:text_typelab                             -15.82060    2.35937
## DimensionDIM4:text_typelab                             -23.10842    2.35937
## DimensionDIM5:text_typelab                             -23.69286    2.35937
## DimensionDIM6:text_typelab                             -25.10841    2.35937
## DimensionDIM2:text_typeclassroom_management_talk_trad  -33.71256    1.84153
## DimensionDIM3:text_typeclassroom_management_talk_trad   -8.94001    1.84153
## DimensionDIM4:text_typeclassroom_management_talk_trad  -19.74919    1.84153
## DimensionDIM5:text_typeclassroom_management_talk_trad  -20.71335    1.84153
## DimensionDIM6:text_typeclassroom_management_talk_trad  -20.84339    1.84153
## DimensionDIM2:text_typeinstructional_video             -21.06559    1.30245
## DimensionDIM3:text_typeinstructional_video              -5.77590    1.30245
## DimensionDIM4:text_typeinstructional_video             -11.52787    1.30245
## DimensionDIM5:text_typeinstructional_video             -13.54583    1.30245
## DimensionDIM6:text_typeinstructional_video             -12.52402    1.30245
## DimensionDIM2:text_typeclassroom_management_talk_tmle  -16.31916    2.05108
## DimensionDIM3:text_typeclassroom_management_talk_tmle   -0.06053    2.05108
## DimensionDIM4:text_typeclassroom_management_talk_tmle  -11.18583    2.05108
## DimensionDIM5:text_typeclassroom_management_talk_tmle  -11.55729    2.05108
## DimensionDIM6:text_typeclassroom_management_talk_tmle  -10.33605    2.05108
##                                                               df t value
## (Intercept)                                             55.70091  -6.004
## DimensionDIM2                                           69.76771  11.359
## DimensionDIM3                                           69.76771   0.692
## DimensionDIM4                                           69.76771   5.197
## DimensionDIM5                                           69.76771   6.147
## DimensionDIM6                                           69.76771   5.729
## text_typeother_institutional_writing                   114.91654  -2.904
## text_typecourse_packs                                 4544.98206  -0.403
## text_typecourse_management                            4546.32244  -0.614
## text_typesyllabus                                     4548.06596  -9.351
## text_typeslides                                       4552.13806  -5.569
## text_typequiz                                         4552.16456  -3.012
## text_typeinstructional_reading                        4548.74629  -1.835
## text_typeassignment_description                       4549.56480  -3.048
## text_typeannouncements_discussions                    4545.46363  -1.246
## text_typestudy_group                                    91.10006  13.857
## text_typeservice_encounter                              37.38425   9.250
## text_typeoffice_hours                                  264.75065  10.350
## text_typelecture                                        19.91037  12.259
## text_typelab                                           147.57334  11.535
## text_typeclassroom_management_talk_trad                 59.14625  11.938
## text_typeinstructional_video                            17.34059  10.862
## text_typeclassroom_management_talk_tmle                 87.93215   5.698
## DimensionDIM2:text_typeother_institutional_writing     137.42522   2.935
## DimensionDIM3:text_typeother_institutional_writing     137.42522   2.932
## DimensionDIM4:text_typeother_institutional_writing     137.42522   1.687
## DimensionDIM5:text_typeother_institutional_writing     137.42522   2.698
## DimensionDIM6:text_typeother_institutional_writing     137.42522   2.467
## DimensionDIM2:text_typecourse_packs                   7716.31337   0.204
## DimensionDIM3:text_typecourse_packs                   7716.31331   1.096
## DimensionDIM4:text_typecourse_packs                   7716.31337   0.158
## DimensionDIM5:text_typecourse_packs                   7716.31336   0.863
## DimensionDIM6:text_typecourse_packs                   7716.31337   0.626
## DimensionDIM2:text_typecourse_management              7706.18679  -1.307
## DimensionDIM3:text_typecourse_management              7706.18676   3.158
## DimensionDIM4:text_typecourse_management              7706.18680   0.243
## DimensionDIM5:text_typecourse_management              7706.18679  -0.110
## DimensionDIM6:text_typecourse_management              7706.18680   0.193
## DimensionDIM2:text_typesyllabus                       7690.79186   4.680
## DimensionDIM3:text_typesyllabus                       7690.79125   8.456
## DimensionDIM4:text_typesyllabus                       7690.79188   6.271
## DimensionDIM5:text_typesyllabus                       7690.79182   6.701
## DimensionDIM6:text_typesyllabus                       7690.79189   6.305
## DimensionDIM2:text_typeslides                         7668.00817   0.969
## DimensionDIM3:text_typeslides                         7668.00771   3.611
## DimensionDIM4:text_typeslides                         7668.00821   3.771
## DimensionDIM5:text_typeslides                         7668.00814   4.000
## DimensionDIM6:text_typeslides                         7668.00821   3.437
## DimensionDIM2:text_typequiz                           7663.70112  -3.523
## DimensionDIM3:text_typequiz                           7663.70045   4.895
## DimensionDIM4:text_typequiz                           7663.70121   1.676
## DimensionDIM5:text_typequiz                           7663.70116   1.357
## DimensionDIM6:text_typequiz                           7663.70122   1.939
## DimensionDIM2:text_typeinstructional_reading          7699.12314  -1.688
## DimensionDIM3:text_typeinstructional_reading          7699.12220   5.283
## DimensionDIM4:text_typeinstructional_reading          7699.12329   1.171
## DimensionDIM5:text_typeinstructional_reading          7699.12314   0.840
## DimensionDIM6:text_typeinstructional_reading          7699.12322   1.100
## DimensionDIM2:text_typeassignment_description         7686.05555  -4.022
## DimensionDIM3:text_typeassignment_description         7686.05477   7.585
## DimensionDIM4:text_typeassignment_description         7686.05565   2.050
## DimensionDIM5:text_typeassignment_description         7686.05556   1.686
## DimensionDIM6:text_typeassignment_description         7686.05566   1.577
## DimensionDIM2:text_typeannouncements_discussions      7711.27272  -4.456
## DimensionDIM3:text_typeannouncements_discussions      7711.27188   9.306
## DimensionDIM4:text_typeannouncements_discussions      7711.27271   0.715
## DimensionDIM5:text_typeannouncements_discussions      7711.27265   0.056
## DimensionDIM6:text_typeannouncements_discussions      7711.27273   0.153
## DimensionDIM2:text_typestudy_group                     138.37693 -19.416
## DimensionDIM3:text_typestudy_group                     138.37693  -9.018
## DimensionDIM4:text_typestudy_group                     138.37693 -12.186
## DimensionDIM5:text_typestudy_group                     138.37693 -12.051
## DimensionDIM6:text_typestudy_group                     138.37693 -13.353
## DimensionDIM2:text_typeservice_encounter                43.91802 -15.181
## DimensionDIM3:text_typeservice_encounter                43.91802  -5.304
## DimensionDIM4:text_typeservice_encounter                43.91802  -9.221
## DimensionDIM5:text_typeservice_encounter                43.91802  -8.857
## DimensionDIM6:text_typeservice_encounter                43.91802  -9.842
## DimensionDIM2:text_typeoffice_hours                    424.94729 -14.231
## DimensionDIM3:text_typeoffice_hours                    424.94730  -5.577
## DimensionDIM4:text_typeoffice_hours                    424.94730  -8.685
## DimensionDIM5:text_typeoffice_hours                    424.94730  -9.065
## DimensionDIM6:text_typeoffice_hours                    424.94730  -9.459
## DimensionDIM2:text_typelecture                          24.47017 -18.395
## DimensionDIM3:text_typelecture                          24.47017  -7.024
## DimensionDIM4:text_typelecture                          24.47017 -11.078
## DimensionDIM5:text_typelecture                          24.47017 -11.477
## DimensionDIM6:text_typelecture                          24.47017 -11.570
## DimensionDIM2:text_typelab                             229.97495 -16.195
## DimensionDIM3:text_typelab                             229.97495  -6.705
## DimensionDIM4:text_typelab                             229.97495  -9.794
## DimensionDIM5:text_typelab                             229.97495 -10.042
## DimensionDIM6:text_typelab                             229.97495 -10.642
## DimensionDIM2:text_typeclassroom_management_talk_trad   86.89739 -18.307
## DimensionDIM3:text_typeclassroom_management_talk_trad   86.89739  -4.855
## DimensionDIM4:text_typeclassroom_management_talk_trad   86.89739 -10.724
## DimensionDIM5:text_typeclassroom_management_talk_trad   86.89739 -11.248
## DimensionDIM6:text_typeclassroom_management_talk_trad   86.89739 -11.319
## DimensionDIM2:text_typeinstructional_video              21.85100 -16.174
## DimensionDIM3:text_typeinstructional_video              21.85100  -4.435
## DimensionDIM4:text_typeinstructional_video              21.85100  -8.851
## DimensionDIM5:text_typeinstructional_video              21.85100 -10.400
## DimensionDIM6:text_typeinstructional_video              21.85100  -9.616
## DimensionDIM2:text_typeclassroom_management_talk_tmle  132.96161  -7.956
## DimensionDIM3:text_typeclassroom_management_talk_tmle  132.96161  -0.030
## DimensionDIM4:text_typeclassroom_management_talk_tmle  132.96161  -5.454
## DimensionDIM5:text_typeclassroom_management_talk_tmle  132.96161  -5.635
## DimensionDIM6:text_typeclassroom_management_talk_tmle  132.96161  -5.039
##                                                       Pr(>|t|)    
## (Intercept)                                           1.52e-07 ***
## DimensionDIM2                                          < 2e-16 ***
## DimensionDIM3                                         0.491424    
## DimensionDIM4                                         1.92e-06 ***
## DimensionDIM5                                         4.35e-08 ***
## DimensionDIM6                                         2.36e-07 ***
## text_typeother_institutional_writing                  0.004420 ** 
## text_typecourse_packs                                 0.686668    
## text_typecourse_management                            0.539055    
## text_typesyllabus                                      < 2e-16 ***
## text_typeslides                                       2.71e-08 ***
## text_typequiz                                         0.002610 ** 
## text_typeinstructional_reading                        0.066543 .  
## text_typeassignment_description                       0.002313 ** 
## text_typeannouncements_discussions                    0.212920    
## text_typestudy_group                                   < 2e-16 ***
## text_typeservice_encounter                            3.33e-11 ***
## text_typeoffice_hours                                  < 2e-16 ***
## text_typelecture                                      9.88e-11 ***
## text_typelab                                           < 2e-16 ***
## text_typeclassroom_management_talk_trad                < 2e-16 ***
## text_typeinstructional_video                          3.68e-09 ***
## text_typeclassroom_management_talk_tmle               1.59e-07 ***
## DimensionDIM2:text_typeother_institutional_writing    0.003908 ** 
## DimensionDIM3:text_typeother_institutional_writing    0.003946 ** 
## DimensionDIM4:text_typeother_institutional_writing    0.093889 .  
## DimensionDIM5:text_typeother_institutional_writing    0.007856 ** 
## DimensionDIM6:text_typeother_institutional_writing    0.014862 *  
## DimensionDIM2:text_typecourse_packs                   0.838118    
## DimensionDIM3:text_typecourse_packs                   0.273278    
## DimensionDIM4:text_typecourse_packs                   0.874367    
## DimensionDIM5:text_typecourse_packs                   0.388157    
## DimensionDIM6:text_typecourse_packs                   0.531658    
## DimensionDIM2:text_typecourse_management              0.191407    
## DimensionDIM3:text_typecourse_management              0.001592 ** 
## DimensionDIM4:text_typecourse_management              0.807898    
## DimensionDIM5:text_typecourse_management              0.912589    
## DimensionDIM6:text_typecourse_management              0.846846    
## DimensionDIM2:text_typesyllabus                       2.92e-06 ***
## DimensionDIM3:text_typesyllabus                        < 2e-16 ***
## DimensionDIM4:text_typesyllabus                       3.78e-10 ***
## DimensionDIM5:text_typesyllabus                       2.21e-11 ***
## DimensionDIM6:text_typesyllabus                       3.05e-10 ***
## DimensionDIM2:text_typeslides                         0.332515    
## DimensionDIM3:text_typeslides                         0.000307 ***
## DimensionDIM4:text_typeslides                         0.000164 ***
## DimensionDIM5:text_typeslides                         6.40e-05 ***
## DimensionDIM6:text_typeslides                         0.000591 ***
## DimensionDIM2:text_typequiz                           0.000429 ***
## DimensionDIM3:text_typequiz                           1.00e-06 ***
## DimensionDIM4:text_typequiz                           0.093780 .  
## DimensionDIM5:text_typequiz                           0.174734    
## DimensionDIM6:text_typequiz                           0.052549 .  
## DimensionDIM2:text_typeinstructional_reading          0.091403 .  
## DimensionDIM3:text_typeinstructional_reading          1.31e-07 ***
## DimensionDIM4:text_typeinstructional_reading          0.241792    
## DimensionDIM5:text_typeinstructional_reading          0.401212    
## DimensionDIM6:text_typeinstructional_reading          0.271555    
## DimensionDIM2:text_typeassignment_description         5.83e-05 ***
## DimensionDIM3:text_typeassignment_description         3.72e-14 ***
## DimensionDIM4:text_typeassignment_description         0.040389 *  
## DimensionDIM5:text_typeassignment_description         0.091855 .  
## DimensionDIM6:text_typeassignment_description         0.114807    
## DimensionDIM2:text_typeannouncements_discussions      8.48e-06 ***
## DimensionDIM3:text_typeannouncements_discussions       < 2e-16 ***
## DimensionDIM4:text_typeannouncements_discussions      0.474904    
## DimensionDIM5:text_typeannouncements_discussions      0.955360    
## DimensionDIM6:text_typeannouncements_discussions      0.878719    
## DimensionDIM2:text_typestudy_group                     < 2e-16 ***
## DimensionDIM3:text_typestudy_group                    1.42e-15 ***
## DimensionDIM4:text_typestudy_group                     < 2e-16 ***
## DimensionDIM5:text_typestudy_group                     < 2e-16 ***
## DimensionDIM6:text_typestudy_group                     < 2e-16 ***
## DimensionDIM2:text_typeservice_encounter               < 2e-16 ***
## DimensionDIM3:text_typeservice_encounter              3.53e-06 ***
## DimensionDIM4:text_typeservice_encounter              7.79e-12 ***
## DimensionDIM5:text_typeservice_encounter              2.50e-11 ***
## DimensionDIM6:text_typeservice_encounter              1.11e-12 ***
## DimensionDIM2:text_typeoffice_hours                    < 2e-16 ***
## DimensionDIM3:text_typeoffice_hours                   4.37e-08 ***
## DimensionDIM4:text_typeoffice_hours                    < 2e-16 ***
## DimensionDIM5:text_typeoffice_hours                    < 2e-16 ***
## DimensionDIM6:text_typeoffice_hours                    < 2e-16 ***
## DimensionDIM2:text_typelecture                        7.74e-16 ***
## DimensionDIM3:text_typelecture                        2.61e-07 ***
## DimensionDIM4:text_typelecture                        5.08e-11 ***
## DimensionDIM5:text_typelecture                        2.44e-11 ***
## DimensionDIM6:text_typelecture                        2.06e-11 ***
## DimensionDIM2:text_typelab                             < 2e-16 ***
## DimensionDIM3:text_typelab                            1.54e-10 ***
## DimensionDIM4:text_typelab                             < 2e-16 ***
## DimensionDIM5:text_typelab                             < 2e-16 ***
## DimensionDIM6:text_typelab                             < 2e-16 ***
## DimensionDIM2:text_typeclassroom_management_talk_trad  < 2e-16 ***
## DimensionDIM3:text_typeclassroom_management_talk_trad 5.27e-06 ***
## DimensionDIM4:text_typeclassroom_management_talk_trad  < 2e-16 ***
## DimensionDIM5:text_typeclassroom_management_talk_trad  < 2e-16 ***
## DimensionDIM6:text_typeclassroom_management_talk_trad  < 2e-16 ***
## DimensionDIM2:text_typeinstructional_video            1.21e-13 ***
## DimensionDIM3:text_typeinstructional_video            0.000212 ***
## DimensionDIM4:text_typeinstructional_video            1.12e-08 ***
## DimensionDIM5:text_typeinstructional_video            6.33e-10 ***
## DimensionDIM6:text_typeinstructional_video            2.62e-09 ***
## DimensionDIM2:text_typeclassroom_management_talk_tmle 6.85e-13 ***
## DimensionDIM3:text_typeclassroom_management_talk_tmle 0.976502    
## DimensionDIM4:text_typeclassroom_management_talk_tmle 2.33e-07 ***
## DimensionDIM5:text_typeclassroom_management_talk_tmle 1.00e-07 ***
## DimensionDIM6:text_typeclassroom_management_talk_tmle 1.50e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

3.7.4 Pairwise Comparison

  • Pairwise comparison through Tukey.
#originally we used comparison and manually reversing the contrast, but here we did so using the emmeans function

#mm_texttype <- emmeans(text_type_1.a, pairwise ~ rev(text_type) | Dimension, adjust = "tukey")
#mm_texttype
mm_texttype.rev3 <- emmeans(text_type_1.b, revpairwise ~ rev(text_type) | Dimension, adjust = "tukey")


#test(mm_texttype.rev3)
as.data.frame(mm_texttype.rev3)

3.7.5 Effect sizes

  • Effect size is shown in cohen’s d.
#text_type_eff <- emmeans::eff_size(mm_texttype, sigma = sigma(text_type_1.a), edf = Inf)
#text_type_eff
text_type_eff.rev3 <- emmeans::eff_size(mm_texttype.rev3, sigma = sigma(text_type_1.b), edf = Inf)
as.data.frame(text_type_eff.rev3)
#write.csv(as.data.frame(text_type_eff.rev3), 'effectsize_newrandom.csv')

3.7.6 Plot of register comparisons

## This is the modified version of comparison plot
plot(mm_texttype.rev3, comparisons = TRUE, CIs = F)  +
  facet_wrap(~Dimension, scale = "free_x", ncol = 1) +
  scale_x_continuous(limits=c(-20,20)) +
  theme_bw() +
  theme(axis.text.x  = element_text(face="bold", size=10)) 

plot(mm_texttype.rev3, comparisons = TRUE, CIs = F)  +
  facet_wrap(~Dimension, scale = "free_x", ncol = 1) +
  theme_bw() +
  theme(axis.text.x  = element_text(face="bold", size = 10)) 

ggsave('comparison.jpeg', width = 6.5, height = 15.5, dpi = 600)